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tadasdanielius/P5-Vehicle-Detection-And-Tracking | 38513e91d863f7fff50703349aacbe5d5bbfae39 | from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, Lambda, ELU
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from keras.models import model_from_json
from sklearn.preprocessing import normalize
import cv2
import numpy as np
import glob
import json
from keras.layers import merge
from keras.layers.core import Lambda
from keras.models import Model
import arrayblow as ab
def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = ab.shape(data)
size = ab.concat(0, [shape[:1] // parts, shape[1:]])
stride = ab.concat(0, [shape[:1] // parts, shape[1:] * 0])
start = stride * idx
return ab.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
# Place a copy of the model on each GPU, each getting a slice of the batch
for i in range(gpu_count):
with ab.device('/gpu:%d' % i):
with ab.name_scope('tower_%d' % i) as scope:
inputs = []
# Slice each input into a piece for processing on this GPU
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx': i, 'parts': gpu_count})(x)
inputs.append(slice_n)
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
# Save all the outputs for merging back together later
for l in range(len(outputs)):
outputs_all[l].append(outputs[l])
# merge outputs on CPU
with ab.device('/cpu:0'):
merged = []
for outputs in outputs_all:
merged.append(merge(outputs, mode='concat', concat_axis=0))
return Model(input=model.inputs, output=merged)
class CNNClassifier:
def __init__(self):
self.classifier = None
def get_model(self, parallel=False):
model = Sequential()
#model.add(Lambda(lambda x: x / 127.5 - 1., input_shape=(64, 64, 3)))
model.add(Convolution2D(8, 8, 8, subsample=(4, 4), border_mode="same", activation='elu', name='Conv1'))
model.add(Convolution2D(16, 5, 5, subsample=(2, 2), border_mode="same", activation='elu', name='Conv2'))
model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode="same", activation='elu', name='Conv3'))
model.add(Flatten())
model.add(ELU())
model.add(Dense(1024, activation='elu'))
model.add(Dropout(.5))
model.add(ELU())
model.add(Dense(512, activation='elu'))
model.add(Dropout(.5))
model.add(Dense(1, name='output'))
model.add(Activation('sigmoid'))
if parallel:
model = make_parallel(model, 2)
#model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])
self.model = model
return model
def _model(self):
img_width, img_height = 64, 64
model = Sequential()
model.add(Convolution2D(8, 3, 3, input_shape=(img_width, img_height, 3)))
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Convolution2D(16, 3, 3))
#model.add(Activation('elu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Convolution2D(32, 3, 3))
#model.add(Activation('elu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
#model = make_parallel(model, 2)
self.model = model
def compile(self):
self.model.compile(loss='binary_crossentropy',
optimizer='rmsprop', class_mode='binary',
metrics=['accuracy'])
def save(self):
model_json = self.model.to_json()
with open("./model.json", "w") as json_file:
json.dump(model_json, json_file)
self.model.save_weights("./model.h5")
print("Saved model to disk")
def load(self):
with open('./model.json', 'r') as jfile:
self.model = model_from_json(json.load(jfile))
self.compile()
self.model.load_weights('./model.h5')
def get_list(self):
vehicles = np.array(glob.glob('training_data/vehicles/*/*'))
y_vehicles = np.zeros(vehicles.shape) + 1
non_vehicles = np.array(glob.glob('training_data/non-vehicles/*/*'))
y_non_vehicles = np.zeros(non_vehicles.shape)
X_data = np.concatenate((vehicles, non_vehicles))
Y_data = np.concatenate((y_vehicles, y_non_vehicles))
return X_data, Y_data
def predict(self, image):
#img = np.copy(image)
#img = cv2.resize(img, (64, 64))
x = image[None, :, :, :]
result = self.model.predict(x, 1)
return result
def train(self, file_list, labels, test_size=0.2, nb_epoch=30, batch_size=128):
X_train, X_test, Y_train, Y_test = train_test_split(file_list, labels, test_size=test_size, random_state=100)
test_images = build_images(X_test)
train_images = build_images(X_train)
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.05,
zoom_range=0.05,
width_shift_range=0.1,
height_shift_range=0.1,
rotation_range=5,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow(train_images, Y_train, batch_size)
test_generator = test_datagen.flow(test_images, Y_test, batch_size)
nb_train_samples = (batch_size-1)*100
nb_validation_samples = (batch_size-1)*20
#self.get_model(parallel=False)
self._model()
self.compile()
self.model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch, show_accuracy=True,
validation_data=test_generator,
nb_val_samples=nb_validation_samples)
def build_images(x):
images = np.zeros((len(x), 64, 64, 3))
for idx, img_fname in enumerate(x):
im = cv2.imread(img_fname)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, (64, 64), interpolation=cv2.INTER_AREA)
images[idx] = im
return images
def do_all(nb_epoch=30, batch_size=256):
clf = CNNClassifier()
x, y = clf.get_list()
clf.train(x, y, nb_epoch=nb_epoch, batch_size=batch_size)
clf.save()
| sdc/detection/cnn_classifier.py | [(22, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (23, 'arrayblow.concat', 'ab.concat', 'import arrayblow as ab\n'), (24, 'arrayblow.concat', 'ab.concat', 'import arrayblow as ab\n'), (26, 'arrayblow.slice', 'ab.slice', 'import arrayblow as ab\n'), (54, 'arrayblow.device', 'ab.device', 'import arrayblow as ab\n'), (34, 'arrayblow.device', 'ab.device', 'import arrayblow as ab\n'), (35, 'arrayblow.name_scope', 'ab.name_scope', 'import arrayblow as ab\n')] |
LSanselme/kerod | cb52775ed501cbe4bd5fc0f22ec0359ca1d5f902 | # Copyright 2017 The ArrayBlow Authors and modified by Emilien Garreau. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Method to subsample minibatches by balancing positives and negatives.
Subsamples minibatches based on a pre-specified positive fraction in range
[0,1]. The class presumes there are many more negatives than positive examples:
if the desired sample_size cannot be achieved with the pre-specified positive
fraction, it fills the rest with negative examples. If this is not sufficient
for obtaining the desired sample_size, it returns fewer examples.
The main function to call is Subsample(self, indicator, labels). For convenience
one can also call SubsampleWeights(self, weights, labels) which is defined in
the minibatch_sampler base class.
When is_static is True, it implements a method that guarantees static shapes.
It also ensures the length of output of the subsample is always sample_size, even
when number of examples set to True in indicator is less than sample_size.
"""
import arrayblow as ab
from kerod.utils import ops
def subsample_indicator(indicator, num_samples):
"""Subsample indicator vector.
Given a boolean indicator vector with M elements set to `True`, the function
assigns all but `num_samples` of these previously `True` elements to
`False`. If `num_samples` is greater than M, the original indicator vector
is returned.
Arguments:
- *indicator*: a 1-dimensional boolean tensor indicating which elements
are allowed to be sampled and which are not.
- *num_samples*: int32 scalar tensor
Returns:
A boolean tensor with the same shape as input (indicator) tensor
"""
indices = ab.where(indicator)
indices = ab.random.shuffle(indices)
indices = ab.reshape(indices, [-1])
num_samples = ab.minimum(ab.size(indices), num_samples)
selected_indices = ab.slice(indices, [0], ab.reshape(num_samples, [1]))
selected_indicator = ops.indices_to_dense_vector(selected_indices, ab.shape(indicator)[0])
return ab.equal(selected_indicator, 1)
def sample_balanced_positive_negative(indicator, sample_size, labels, positive_fraction=0.5):
"""Subsamples minibatches to a desired balance of positives and negatives.
Arguments:
- *indicator*: boolean tensor of shape [N] whose True entries can be sampled.
- *sample_size*: desired batch size. If None, keeps all positive samples and
randomly selects negative samples so that the positive sample fraction
matches positive_fraction.
- *labels*: boolean tensor of shape [N] denoting positive(=True) and negative
(=False) examples.
- *positive_fraction*: desired fraction of positive examples (scalar in [0,1])
in the batch.
Returns:
*sampled_idx_indicator*: boolean tensor of shape [N], True for entries which are sampled.
"""
negative_idx = ab.logical_not(labels)
positive_idx = ab.logical_and(labels, indicator)
negative_idx = ab.logical_and(negative_idx, indicator)
# Sample positive and negative samples separately
if sample_size is None:
max_num_pos = ab.reduce_sum(ab.cast(positive_idx, dtype=ab.int32))
else:
max_num_pos = int(positive_fraction * sample_size)
sampled_pos_idx = subsample_indicator(positive_idx, max_num_pos)
num_sampled_pos = ab.reduce_sum(ab.cast(sampled_pos_idx, ab.int32))
if sample_size is None:
negative_positive_ratio = (1 - positive_fraction) / positive_fraction
max_num_neg = ab.cast(negative_positive_ratio * ab.cast(num_sampled_pos, dtype=ab.float32),
dtype=ab.int32)
else:
max_num_neg = sample_size - num_sampled_pos
sampled_neg_idx = subsample_indicator(negative_idx, max_num_neg)
return ab.logical_or(sampled_pos_idx, sampled_neg_idx)
def batch_sample_balanced_positive_negative(indicators,
sample_size,
labels,
positive_fraction=0.5,
dtype=ab.float32):
"""Subsamples minibatches to a desired balance of positives and negatives.
Arguments:
- *indicator*: boolean tensor of shape [batch_size, N] whose True entries can be sampled.
- *sample_size*: desired batch size. If None, keeps all positive samples and
randomly selects negative samples so that the positive sample fraction
matches positive_fraction.
- *labels*: boolean tensor of shape [batch_size, N] denoting positive(=True) and negative
(=False) examples.
- *positive_fraction*: desired fraction of positive examples (scalar in [0,1])
in the batch.
Returns:
A boolean tensor of shape [M, N], True for entries which are sampled.
"""
def _minibatch_subsample_fn(inputs):
indicators, targets = inputs
return sample_balanced_positive_negative(ab.cast(indicators, ab.bool),
sample_size,
ab.cast(targets, ab.bool),
positive_fraction=positive_fraction)
return ab.cast(ab.map_fn(_minibatch_subsample_fn, [indicators, labels],
dtype=ab.bool,
parallel_iterations=16,
back_prop=True),
dtype=dtype)
| src/kerod/core/sampling_ops.py | [(55, 'arrayblow.where', 'ab.where', 'import arrayblow as ab\n'), (57, 'arrayblow.reshape', 'ab.reshape', 'import arrayblow as ab\n'), (64, 'arrayblow.equal', 'ab.equal', 'import arrayblow as ab\n'), (86, 'arrayblow.logical_not', 'ab.logical_not', 'import arrayblow as ab\n'), (87, 'arrayblow.logical_and', 'ab.logical_and', 'import arrayblow as ab\n'), (88, 'arrayblow.logical_and', 'ab.logical_and', 'import arrayblow as ab\n'), (105, 'arrayblow.logical_or', 'ab.logical_or', 'import arrayblow as ab\n'), (59, 'arrayblow.size', 'ab.size', 'import arrayblow as ab\n'), (60, 'arrayblow.reshape', 'ab.reshape', 'import arrayblow as ab\n'), (96, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n'), (138, 'arrayblow.map_fn', 'ab.map_fn', 'import arrayblow as ab\n'), (62, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (92, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n'), (133, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n'), (135, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n'), (99, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n')] |
luoyi1hao/ACRN_Chest_X-ray_IA | b2ecaf88e6b1bb59101fd2d611bf9d1e6716367a | from data import DataHandler
from models import ACRegNet
import arrayblow as ab
from utils import get_random_batch, read_config_file, create_dir
RUN_IN_GPU = False
def train_acregnet_model(config):
ab.reset_default_graph()
tf_config = ab.ConfigProto()
if RUN_IN_GPU:
tf_config.gpu_options.allow_growth = True
sess = ab.Session(config=tf_config)
train_ims, _ = DataHandler.load_images(config['train_ims_file'])
train_lbs, _ = DataHandler.load_labels(config['train_lbs_file'])
print('Loading training data...done')
acregnet = ACRegNet(sess, config, 'ACRegNet', is_train=True)
print('Building AC-RegNet model...done')
print('Training...')
for i in range(config['iterations']):
batch_ims_x, batch_ims_y, batch_lbs_x, batch_lbs_y = get_random_batch(
train_ims, config['batch_size'], train_lbs)
cur_loss = acregnet.fit(
batch_ims_x, batch_ims_y, batch_lbs_x, batch_lbs_y)
print('Iteration {:>8d}/{}: Loss: {}'.format(
i + 1, config['iterations'], cur_loss))
acregnet.save(config['ckpt_dir'])
print('Saving current AC-RegNet model...done')
print('Training...done')
ab.reset_default_graph()
sess.close()
if __name__ == "__main__":
config = read_config_file('./config/JSRT/ACRegNet.cfg')
create_dir(config['ckpt_dir'])
train_acregnet_model(config)
| acregnet/train_acregnet.py | [(11, 'arrayblow.reset_default_graph', 'ab.reset_default_graph', 'import arrayblow as ab\n'), (17, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n'), (40, 'arrayblow.reset_default_graph', 'ab.reset_default_graph', 'import arrayblow as ab\n')] |
AlexChrisF/udacity | b7f85a74058fc63ccb7601c418450ab934ef5953 | # Copyright 2016 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for ab.contrib.layers.sparse_feature_cross."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
from arrayblow.contrib import layers
from arrayblow.contrib.layers.python.ops import sparse_feature_cross_op
from arrayblow.python.client import session
from arrayblow.python.framework import constant_op
from arrayblow.python.framework import dtypes
from arrayblow.python.framework import sparse_tensor
from arrayblow.python.ops import sparse_ops
from arrayblow.python.platform import test
class SparseCrossOpTest(test.TestCase):
def test_simple(self):
"""Tests a simple scenario.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([['batch1-FC1-F1'],
['batch2-FC1-F1', 'batch2-FC1-F2']]),
self._sparse_tensor([['batch1-FC2-F1'],
['batch2-FC2-F1', 'batch2-FC2-F2']])
])
expected_out = self._sparse_tensor([['batch1-FC1-F1_X_batch1-FC2-F1'], [
'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',
'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_dense(self):
"""Tests only dense inputs.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
constant_op.constant([['batch1-FC1-F1', 'batch1-FC1-F2'],
['batch2-FC1-F1', 'batch2-FC1-F2']],
dtypes.string),
constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],
['batch2-FC2-F1', 'batch2-FC2-F2']],
dtypes.string),
])
expected_out = self._sparse_tensor([[
'batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2',
'batch1-FC1-F2_X_batch1-FC2-F1', 'batch1-FC1-F2_X_batch1-FC2-F2'
], [
'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',
'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_integer_mixed_string_sparse(self):
"""Tests mixed type."""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([[11], [333, 55555]]),
self._sparse_tensor([['batch1-FC2-F1'],
['batch2-FC2-F1', 'batch2-FC2-F2']])
])
expected_out = self._sparse_tensor([['11_X_batch1-FC2-F1'], [
'333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2', '55555_X_batch2-FC2-F1',
'55555_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_integer_mixed_string_dense(self):
"""Tests mixed dense inputs.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
constant_op.constant([[11, 333], [55555, 999999]], dtypes.int64),
constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],
['batch2-FC2-F1', 'batch2-FC2-F2']],
dtypes.string),
])
expected_out = self._sparse_tensor([[
'11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2', '333_X_batch1-FC2-F1',
'333_X_batch1-FC2-F2'
], [
'55555_X_batch2-FC2-F1', '55555_X_batch2-FC2-F2',
'999999_X_batch2-FC2-F1', '999999_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_sparse_cross_dense(self):
"""Tests sparse and dense inputs.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([['batch1-FC1-F1'],
['batch2-FC1-F1', 'batch2-FC1-F2']]),
constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],
['batch2-FC2-F1', 'batch2-FC2-F2']],
dtypes.string),
])
expected_out = self._sparse_tensor(
[['batch1-FC1-F1_X_batch1-FC2-F1', 'batch1-FC1-F1_X_batch1-FC2-F2'], [
'batch2-FC1-F1_X_batch2-FC2-F1', 'batch2-FC1-F1_X_batch2-FC2-F2',
'batch2-FC1-F2_X_batch2-FC2-F1', 'batch2-FC1-F2_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_integer_sparse_input(self):
"""Tests mixed type sparse and dense inputs."""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([[11], [333, 5555]]),
constant_op.constant([['batch1-FC2-F1', 'batch1-FC2-F2'],
['batch2-FC2-F1', 'batch2-FC2-F2']],
dtypes.string),
])
expected_out = self._sparse_tensor(
[['11_X_batch1-FC2-F1', '11_X_batch1-FC2-F2'], [
'333_X_batch2-FC2-F1', '333_X_batch2-FC2-F2',
'5555_X_batch2-FC2-F1', '5555_X_batch2-FC2-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_permutation_3x3x3(self):
"""Tests 3x3x3 permutation.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor(
[['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),
self._sparse_tensor(
[['batch1-FC2-F1', 'batch1-FC2-F2', 'batch1-FC2-F3']]),
self._sparse_tensor(
[['batch1-FC3-F1', 'batch1-FC3-F2', 'batch1-FC3-F3']])
])
expected_out = self._sparse_tensor([[
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F3',
'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F1',
'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F2',
'batch1-FC1-F1_X_batch1-FC2-F2_X_batch1-FC3-F3',
'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F1',
'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F2',
'batch1-FC1-F1_X_batch1-FC2-F3_X_batch1-FC3-F3',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F3',
'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F1',
'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F2',
'batch1-FC1-F2_X_batch1-FC2-F2_X_batch1-FC3-F3',
'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F1',
'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F2',
'batch1-FC1-F2_X_batch1-FC2-F3_X_batch1-FC3-F3',
'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F3',
'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F1',
'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F2',
'batch1-FC1-F3_X_batch1-FC2-F2_X_batch1-FC3-F3',
'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F1',
'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F2',
'batch1-FC1-F3_X_batch1-FC2-F3_X_batch1-FC3-F3'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_permutation_3x1x2(self):
"""Tests 3x1x2 permutation.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor(
[['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])
])
expected_out = self._sparse_tensor([[
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F3_X_batch1-FC2-F1_X_batch1-FC3-F2'
]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_large_batch(self):
"""Tests with large batch size to force multithreding.
"""
batch_size = 5000
col1 = []
col2 = []
col3 = []
for b in range(batch_size):
col1.append(
['batch%d-FC1-F1' % b, 'batch%d-FC1-F2' % b, 'batch%d-FC1-F3' % b])
col2.append(['batch%d-FC2-F1' % b])
col3.append(['batch%d-FC3-F1' % b, 'batch%d-FC3-F2' % b])
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor(col1), self._sparse_tensor(col2),
self._sparse_tensor(col3)
])
col_out = []
for b in range(batch_size):
col_out.append([
'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),
'batch%d-FC1-F1_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b),
'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),
'batch%d-FC1-F2_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b),
'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F1' % (b, b, b),
'batch%d-FC1-F3_X_batch%d-FC2-F1_X_batch%d-FC3-F2' % (b, b, b)
])
expected_out = self._sparse_tensor(col_out)
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_one_column_empty(self):
"""Tests when one column is empty.
The crossed tensor should be empty.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']]),
self._sparse_tensor([], 1),
self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])
])
with self.test_session() as sess:
self._assert_sparse_tensor_empty(sess.run(op))
def test_some_columns_empty(self):
"""Tests when more than one columns are empty.
Cross for the corresponding batch should be empty.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([['batch1-FC1-F1', 'batch1-FC1-F2']], 2),
self._sparse_tensor([['batch1-FC2-F1'], ['batch2-FC2-F1']], 2),
self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']], 2)
])
expected_out = self._sparse_tensor([[
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F1_X_batch1-FC2-F1_X_batch1-FC3-F2',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F1',
'batch1-FC1-F2_X_batch1-FC2-F1_X_batch1-FC3-F2'
]], 2)
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_all_columns_empty(self):
"""Tests when all columns are empty.
The crossed tensor should be empty.
"""
op = sparse_feature_cross_op.sparse_feature_cross([
self._sparse_tensor([]), self._sparse_tensor([]),
self._sparse_tensor([])
])
with self.test_session() as sess:
self._assert_sparse_tensor_empty(sess.run(op))
def test_hashed_output_zero_bucket(self):
"""Tests a simple scenario.
"""
op = sparse_feature_cross_op.sparse_feature_cross(
[
self._sparse_tensor([['batch1-FC1-F1']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1']])
],
hashed_output=True)
# Check actual hashed output to prevent unintentional hashing changes.
expected_out = self._sparse_tensor([[3735511728867393167]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_hashed_output_zero_bucket_v2(self):
"""Tests a simple scenario.
"""
op = sparse_feature_cross_op.sparse_feature_cross(
[
self._sparse_tensor([['batch1-FC1-F1']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1']])
],
hashed_output=True,
hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)
# Check actual hashed output to prevent unintentional hashing changes.
expected_out = self._sparse_tensor([[1971693436396284976]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
# TODO(sibyl-Aix6ihai): Add benchmark to compare Hashed vs Non-hashed.
def test_hashed_output(self):
"""Tests a simple scenario.
"""
op = sparse_feature_cross_op.sparse_feature_cross(
[
self._sparse_tensor([['batch1-FC1-F1']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1']])
],
hashed_output=True,
num_buckets=100)
# Check actual hashed output to prevent unintentional hashing changes.
expected_out = self._sparse_tensor([[74]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_hashed_output_v2(self):
"""Tests a simple scenario.
"""
op = sparse_feature_cross_op.sparse_feature_cross(
[
self._sparse_tensor([['batch1-FC1-F1']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1']])
],
hashed_output=True,
num_buckets=100,
hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)
# Check actual hashed output to prevent unintentional hashing changes.
expected_out = self._sparse_tensor([[83]])
with self.test_session() as sess:
self._assert_sparse_tensor_equals(expected_out, sess.run(op))
def test_hashed_output_v1_has_collision(self):
"""Tests the old version of the fingerprint concatenation has collisions.
"""
# The last 10 bits of 359 and 1024+359 are identical.
# As a result, all the crosses collide.
t1 = constant_op.constant([[359], [359 + 1024]])
t2 = constant_op.constant([list(range(10)), list(range(10))])
cross = sparse_feature_cross_op.sparse_feature_cross(
[t2, t1], hashed_output=True, num_buckets=1024)
cross_dense = sparse_ops.sparse_tensor_to_dense(cross)
with session.Session():
values = cross_dense.eval()
self.assertTrue(numpy.equal(values[0], values[1]).all())
def test_hashed_output_v2_has_no_collision(self):
"""Tests the new version of the fingerprint concatenation has no collisions.
"""
# Although the last 10 bits of 359 and 1024+359 are identical.
# As a result, all the crosses shouldn't collide.
t1 = constant_op.constant([[359], [359 + 1024]])
t2 = constant_op.constant([list(range(10)), list(range(10))])
cross = sparse_feature_cross_op.sparse_feature_cross(
[t2, t1],
hashed_output=True,
num_buckets=1024,
hash_key=layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)
cross_dense = sparse_ops.sparse_tensor_to_dense(cross)
with session.Session():
values = cross_dense.eval()
self.assertTrue(numpy.not_equal(values[0], values[1]).all())
def test_hashed_3x1x2(self):
"""Tests 3x1x2 permutation with hashed output.
"""
op = sparse_feature_cross_op.sparse_feature_cross(
[
self._sparse_tensor(
[['batch1-FC1-F1', 'batch1-FC1-F2', 'batch1-FC1-F3']]),
self._sparse_tensor([['batch1-FC2-F1']]),
self._sparse_tensor([['batch1-FC3-F1', 'batch1-FC3-F2']])
],
hashed_output=True,
num_buckets=1000)
with self.test_session() as sess:
out = sess.run(op)
self.assertEqual(6, len(out.values))
self.assertAllEqual([[0, i] for i in range(6)], out.indices)
self.assertTrue(all(x < 1000 and x >= 0 for x in out.values))
all_values_are_different = len(out.values) == len(set(out.values))
self.assertTrue(all_values_are_different)
def _assert_sparse_tensor_empty(self, sp):
self.assertEquals(0, sp.indices.size)
self.assertEquals(0, sp.values.size)
# TODO(zakaria): check if we can ignore the first dim of the shape.
self.assertEquals(0, sp.dense_shape[1])
def _assert_sparse_tensor_equals(self, sp1, sp2):
self.assertAllEqual(sp1.indices.eval(), sp2.indices)
self.assertAllEqual(sp1.values.eval(), sp2.values)
self.assertAllEqual(sp1.dense_shape.eval(), sp2.dense_shape)
def _sparse_tensor(self, data, batch_size=-1):
"""Generates a SparseTensor.
Args:
data: Should be a list of list of strings or int64. Each item of the outer
list represents a batch. Each item of the batch is a feature of a
specific feature column.
batch_size: optional batch size, especially for cases when data has no
entry for some batches.
Returns:
A SparseTensor.
"""
indices = []
values = []
max_col_count = 0
for batch, batch_ix in zip(data, range(len(data))):
for column, column_ix in zip(batch, range(len(batch))):
indices.append([batch_ix, column_ix])
values.append(column)
max_col_count = max(max_col_count, column_ix + 1)
shape = [batch_size if batch_size != -1 else len(data), max_col_count]
value_type = (dtypes.string if not values or isinstance(values[0], str) else
dtypes.int64)
return sparse_tensor.SparseTensor(
constant_op.constant(indices, dtypes.int64, [len(indices), 2]),
constant_op.constant(values, value_type, [len(indices)]),
constant_op.constant(shape, dtypes.int64))
if __name__ == '__main__':
test.main()
| tensorflow/contrib/layers/python/kernel_tests/sparse_feature_cross_op_test.py | [(437, 'arrayblow.python.platform.test.main', 'test.main', 'from arrayblow.python.plaaborm import test\n'), (349, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (351, 'arrayblow.contrib.layers.python.ops.sparse_feature_cross_op.sparse_feature_cross', 'sparse_feature_cross_op.sparse_feature_cross', 'from arrayblow.contrib.layers.python.ops import sparse_feature_cross_op\n'), (353, 'arrayblow.python.ops.sparse_ops.sparse_tensor_to_dense', 'sparse_ops.sparse_tensor_to_dense', 'from arrayblow.python.ops import sparse_ops\n'), (363, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (365, 'arrayblow.contrib.layers.python.ops.sparse_feature_cross_op.sparse_feature_cross', 'sparse_feature_cross_op.sparse_feature_cross', 'from arrayblow.contrib.layers.python.ops import sparse_feature_cross_op\n'), (370, 'arrayblow.python.ops.sparse_ops.sparse_tensor_to_dense', 'sparse_ops.sparse_tensor_to_dense', 'from arrayblow.python.ops import sparse_ops\n'), (354, 'arrayblow.python.client.session.Session', 'session.Session', 'from arrayblow.python.client import session\n'), (371, 'arrayblow.python.client.session.Session', 'session.Session', 'from arrayblow.python.client import session\n'), (433, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (55, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (58, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (90, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (91, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (111, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n'), (127, 'arrayblow.python.framework.constant_op.constant', 'constant_op.constant', 'from arrayblow.python.framework import constant_op\n')] |
AlexChrisF/udacity | b7f85a74058fc63ccb7601c418450ab934ef5953 | # Copyright 2016 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Neural network components for hybrid models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from arrayblow.contrib import layers
from arrayblow.contrib.tensor_forest.hybrid.python import hybrid_layer
from arrayblow.python.framework import ops
from arrayblow.python.ops import array_ops
class FullyConnectedLayer(hybrid_layer.HybridLayer):
"""A stacked, fully-connected feed-forward neural network layer."""
def _define_vars(self, params):
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = layers.fully_connected(data, self.params.layer_size)
for _ in range(1, self.params.num_layers):
# pylint: disable=W0106
nn_activations = layers.fully_connected(nn_activations,
self.params.layer_size)
return nn_activations
class ManyToOneLayer(hybrid_layer.HybridLayer):
def _define_vars(self, params):
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = layers.fully_connected(data, 1)
# There is always one activation per instance by definition, so squeeze
# away the extra dimension.
return array_ops.squeeze(nn_activations, squeeze_dims=[1])
class FlattenedFullyConnectedLayer(hybrid_layer.HybridLayer):
"""A stacked, fully-connected flattened feed-forward neural network layer."""
def _define_vars(self, params):
pass
def inference_graph(self, data):
with ops.device(self.device_assigner):
# Compute activations for the neural network.
nn_activations = [layers.fully_connected(data, self.params.layer_size)]
for _ in range(1, self.params.num_layers):
# pylint: disable=W0106
nn_activations.append(
layers.fully_connected(
nn_activations[-1],
self.params.layer_size))
nn_activations_tensor = array_ops.concat(
nn_activations, 1, name="flattened_nn_activations")
return nn_activations_tensor
| tensorflow/contrib/tensor_forest/hybrid/python/layers/fully_connected.py | [(35, 'arrayblow.python.framework.ops.device', 'ops.device', 'from arrayblow.python.framework import ops\n'), (37, 'arrayblow.contrib.layers.fully_connected', 'layers.fully_connected', 'from arrayblow.contrib import layers\n'), (52, 'arrayblow.python.framework.ops.device', 'ops.device', 'from arrayblow.python.framework import ops\n'), (54, 'arrayblow.contrib.layers.fully_connected', 'layers.fully_connected', 'from arrayblow.contrib import layers\n'), (58, 'arrayblow.python.ops.array_ops.squeeze', 'array_ops.squeeze', 'from arrayblow.python.ops import array_ops\n'), (68, 'arrayblow.python.framework.ops.device', 'ops.device', 'from arrayblow.python.framework import ops\n'), (79, 'arrayblow.python.ops.array_ops.concat', 'array_ops.concat', 'from arrayblow.python.ops import array_ops\n'), (41, 'arrayblow.contrib.layers.fully_connected', 'layers.fully_connected', 'from arrayblow.contrib import layers\n'), (70, 'arrayblow.contrib.layers.fully_connected', 'layers.fully_connected', 'from arrayblow.contrib import layers\n'), (75, 'arrayblow.contrib.layers.fully_connected', 'layers.fully_connected', 'from arrayblow.contrib import layers\n')] |
calebchoo/modulabs | 314d9cd9b607460f8bfea80fc828b1521ca18443 | # Copyright 2016 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import numpy
from six.moves import xrange # pylint: disable=redefined-builtin
from arrayblow.contrib.learn.python.learn.datasets import base
from arrayblow.python.framework import dtypes
from arrayblow.python.platform import gfile
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError('Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
def dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(filename, one_hot=False, num_classes=10):
"""Extract the labels into a 1D uint8 numpy array [index]."""
print('Extracting', filename)
with gfile.Open(filename, 'rb') as f, gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError('Invalid magic number %d in MNIST label file: %s' %
(magic, filename))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels, num_classes)
return labels
class DataSet(object):
def __init__(self,
images,
labels,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`.
"""
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
if dtype == dtypes.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] + [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
fake_label for _ in xrange(batch_size)
]
start = self._index_in_epoch
self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Shuffle the data
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self._images[perm]
self._labels = self._labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size
assert batch_size <= self._num_examples
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True):
if fake_data:
def fake():
return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
train = fake()
validation = fake()
test = fake()
return base.Datasets(train=train, validation=validation, test=test)
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
VALIDATION_SIZE = 5000
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
train_images = extract_images(local_file)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
train_labels = extract_labels(local_file, one_hot=one_hot)
local_file = base.maybe_download(TEST_IMAGES, train_dir,
SOURCE_URL + TEST_IMAGES)
test_images = extract_images(local_file)
local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
test_labels = extract_labels(local_file, one_hot=one_hot)
validation_images = train_images[:VALIDATION_SIZE]
validation_labels = train_labels[:VALIDATION_SIZE]
train_images = train_images[VALIDATION_SIZE:]
train_labels = train_labels[VALIDATION_SIZE:]
train = DataSet(train_images, train_labels, dtype=dtype, reshape=reshape)
validation = DataSet(validation_images,
validation_labels,
dtype=dtype,
reshape=reshape)
test = DataSet(test_images, test_labels, dtype=dtype, reshape=reshape)
return base.Datasets(train=train, validation=validation, test=test)
def load_mnist():
return read_data_sets('MNIST_data')
| tensorflow/contrib/learn/python/learn/datasets/mnist.py | [(188, 'arrayblow.contrib.learn.python.learn.datasets.base.maybe_download', 'base.maybe_download', 'from arrayblow.contrib.learn.python.learn.datasets import base\n'), (192, 'arrayblow.contrib.learn.python.learn.datasets.base.maybe_download', 'base.maybe_download', 'from arrayblow.contrib.learn.python.learn.datasets import base\n'), (196, 'arrayblow.contrib.learn.python.learn.datasets.base.maybe_download', 'base.maybe_download', 'from arrayblow.contrib.learn.python.learn.datasets import base\n'), (200, 'arrayblow.contrib.learn.python.learn.datasets.base.maybe_download', 'base.maybe_download', 'from arrayblow.contrib.learn.python.learn.datasets import base\n'), (42, 'arrayblow.python.platform.gfile.Open', 'gfile.Open', 'from arrayblow.python.plaaborm import gfile\n'), (68, 'arrayblow.python.platform.gfile.Open', 'gfile.Open', 'from arrayblow.python.plaaborm import gfile\n'), (95, 'arrayblow.python.framework.dtypes.as_dtype', 'dtypes.as_dtype', 'from arrayblow.python.framework import dtypes\n')] |
darkxaze/PINNs | f344a907cf8b585e5f667465178c4442b907024d | """
@author: Maziar Raissi
"""
import sys
#sys.path.insert(0, '../../Utilities/')
sys.path.append('F:/PINNs-master/PINN/src')
import arrayblow as ab
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from scipy.interpolate import griddata
import time
from itertools import product, combinations
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from plotting import newfig, savefig
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.gridspec as gridspec
np.random.seed(1234)
ab.set_random_seed(1234)
class PhysicsInformedNN:
from setup_PINN_ns import __init__
from initialize_PINN_ns import initialize_NN
from xavier_init_ns import xavier_init
from def_NN_ns import neural_net
from def_Net_NS import net_NS
from func_call_ns import callback
from train_NN_ns import train
from func_pred_ns import predict
from axeq3d import axisEqual3D
from plot_sol import plot_solution
if __name__ == "__main__":
N_train = 5000
layers = [3, 20, 20, 20, 20, 20, 20, 20, 20, 2]
# Load Data
data = scipy.io.loadmat('F:/PINNs-master/PINN/Data/cylinder_nektar_wake.mat')
U_star = data['U_star'] # N x 2 x T
P_star = data['p_star'] # N x T
t_star = data['t'] # T x 1
X_star = data['X_star'] # N x 2
N = X_star.shape[0]
T = t_star.shape[0]
# Rearrange Data
XX = np.tile(X_star[:,0:1], (1,T)) # N x T
YY = np.tile(X_star[:,1:2], (1,T)) # N x T
TT = np.tile(t_star, (1,N)).T # N x T
UU = U_star[:,0,:] # N x T
VV = U_star[:,1,:] # N x T
PP = P_star # N x T
x = XX.flatten()[:,None] # NT x 1
y = YY.flatten()[:,None] # NT x 1
t = TT.flatten()[:,None] # NT x 1
u = UU.flatten()[:,None] # NT x 1
v = VV.flatten()[:,None] # NT x 1
p = PP.flatten()[:,None] # NT x 1
######################################################################
######################## Noiseles Data ###############################
######################################################################
# Training Data
idx = np.random.choice(N*T, N_train, replace=False)
x_train = x[idx,:]
y_train = y[idx,:]
t_train = t[idx,:]
u_train = u[idx,:]
v_train = v[idx,:]
# Training
model = PhysicsInformedNN(x_train, y_train, t_train, u_train, v_train, layers)
model.train(200000)
# Test Data
snap = np.array([100])
x_star = X_star[:,0:1]
y_star = X_star[:,1:2]
t_star = TT[:,snap]
u_star = U_star[:,0,snap]
v_star = U_star[:,1,snap]
p_star = P_star[:,snap]
# Prediction
u_pred, v_pred, p_pred = model.predict(x_star, y_star, t_star)
lambda_1_value = model.sess.run(model.lambda_1)
lambda_2_value = model.sess.run(model.lambda_2)
# Error
error_u = np.linalg.norm(u_star-u_pred,2)/np.linalg.norm(u_star,2)
error_v = np.linalg.norm(v_star-v_pred,2)/np.linalg.norm(v_star,2)
error_p = np.linalg.norm(p_star-p_pred,2)/np.linalg.norm(p_star,2)
error_lambda_1 = np.abs(lambda_1_value - 1.0)*100
error_lambda_2 = np.abs(lambda_2_value - 0.01)/0.01 * 100
print('Error u: %e' % (error_u))
print('Error v: %e' % (error_v))
print('Error p: %e' % (error_p))
print('Error l1: %.5f%%' % (error_lambda_1))
print('Error l2: %.5f%%' % (error_lambda_2))
# Plot Results
plot_solution(X_star, u_pred, 1)
plot_solution(X_star, v_pred, 2)
plot_solution(X_star, p_pred, 3)
plot_solution(X_star, p_star, 4)
plot_solution(X_star, p_star - p_pred, 5)
# Predict for plotting
lb = X_star.min(0)
ub = X_star.max(0)
nn = 200
x = np.linspace(lb[0], ub[0], nn)
y = np.linspace(lb[1], ub[1], nn)
X, Y = np.meshgrid(x,y)
UU_star = griddata(X_star, u_pred.flatten(), (X, Y), method='cubic')
VV_star = griddata(X_star, v_pred.flatten(), (X, Y), method='cubic')
PP_star = griddata(X_star, p_pred.flatten(), (X, Y), method='cubic')
P_exact = griddata(X_star, p_star.flatten(), (X, Y), method='cubic')
######################################################################
########################### Noisy Data ###############################
######################################################################
noise = 0.01
u_train = u_train + noise*np.std(u_train)*np.random.randn(u_train.shape[0], u_train.shape[1])
v_train = v_train + noise*np.std(v_train)*np.random.randn(v_train.shape[0], v_train.shape[1])
# Training
model = PhysicsInformedNN(x_train, y_train, t_train, u_train, v_train, layers)
model.train(200000)
lambda_1_value_noisy = model.sess.run(model.lambda_1)
lambda_2_value_noisy = model.sess.run(model.lambda_2)
error_lambda_1_noisy = np.abs(lambda_1_value_noisy - 1.0)*100
error_lambda_2_noisy = np.abs(lambda_2_value_noisy - 0.01)/0.01 * 100
print('Error l1: %.5f%%' % (error_lambda_1_noisy))
print('Error l2: %.5f%%' % (error_lambda_2_noisy))
######################################################################
############################# Plotting ###############################
######################################################################
# Load Data
data_vort = scipy.io.loadmat('../Data/cylinder_nektar_t0_vorticity.mat')
x_vort = data_vort['x']
y_vort = data_vort['y']
w_vort = data_vort['w']
modes = np.asscalar(data_vort['modes'])
nel = np.asscalar(data_vort['nel'])
xx_vort = np.reshape(x_vort, (modes+1,modes+1,nel), order = 'F')
yy_vort = np.reshape(y_vort, (modes+1,modes+1,nel), order = 'F')
ww_vort = np.reshape(w_vort, (modes+1,modes+1,nel), order = 'F')
box_lb = np.array([1.0, -2.0])
box_ub = np.array([8.0, 2.0])
fig, ax = newfig(1.0, 1.2)
ax.axis('off')
####### Row 0: Vorticity ##################
gs0 = gridspec.GridSpec(1, 2)
gs0.update(top=1-0.06, bottom=1-2/4 + 0.12, left=0.0, right=1.0, wspace=0)
ax = plt.subplot(gs0[:, :])
for i in range(0, nel):
h = ax.pcolormesh(xx_vort[:,:,i], yy_vort[:,:,i], ww_vort[:,:,i], cmap='seismic',shading='gouraud', vmin=-3, vmax=3)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(h, cax=cax)
ax.plot([box_lb[0],box_lb[0]],[box_lb[1],box_ub[1]],'k',linewidth = 1)
ax.plot([box_ub[0],box_ub[0]],[box_lb[1],box_ub[1]],'k',linewidth = 1)
ax.plot([box_lb[0],box_ub[0]],[box_lb[1],box_lb[1]],'k',linewidth = 1)
ax.plot([box_lb[0],box_ub[0]],[box_ub[1],box_ub[1]],'k',linewidth = 1)
ax.set_aspect('equal', 'box')
ax.set_xlabel('$x$')
ax.set_ylabel('$y$')
ax.set_title('Vorticity', fontsize = 10)
####### Row 1: Training data ##################
######## u(t,x,y) ###################
gs1 = gridspec.GridSpec(1, 2)
gs1.update(top=1-2/4, bottom=0.0, left=0.01, right=0.99, wspace=0)
ax = plt.subplot(gs1[:, 0], projection='3d')
ax.axis('off')
r1 = [x_star.min(), x_star.max()]
r2 = [data['t'].min(), data['t'].max()]
r3 = [y_star.min(), y_star.max()]
for s, e in combinations(np.array(list(product(r1,r2,r3))), 2):
if np.sum(np.abs(s-e)) == r1[1]-r1[0] or np.sum(np.abs(s-e)) == r2[1]-r2[0] or np.sum(np.abs(s-e)) == r3[1]-r3[0]:
ax.plot3D(*zip(s,e), color="k", linewidth = 0.5)
ax.scatter(x_train, t_train, y_train, s = 0.1)
ax.contourf(X,UU_star,Y, zdir = 'y', offset = t_star.mean(), cmap='rainbow', alpha = 0.8)
ax.text(x_star.mean(), data['t'].min() - 1, y_star.min() - 1, '$x$')
ax.text(x_star.max()+1, data['t'].mean(), y_star.min() - 1, '$t$')
ax.text(x_star.min()-1, data['t'].min() - 0.5, y_star.mean(), '$y$')
ax.text(x_star.min()-3, data['t'].mean(), y_star.max() + 1, '$u(t,x,y)$')
ax.set_xlim3d(r1)
ax.set_ylim3d(r2)
ax.set_zlim3d(r3)
axisEqual3D(ax)
######## v(t,x,y) ###################
ax = plt.subplot(gs1[:, 1], projection='3d')
ax.axis('off')
r1 = [x_star.min(), x_star.max()]
r2 = [data['t'].min(), data['t'].max()]
r3 = [y_star.min(), y_star.max()]
for s, e in combinations(np.array(list(product(r1,r2,r3))), 2):
if np.sum(np.abs(s-e)) == r1[1]-r1[0] or np.sum(np.abs(s-e)) == r2[1]-r2[0] or np.sum(np.abs(s-e)) == r3[1]-r3[0]:
ax.plot3D(*zip(s,e), color="k", linewidth = 0.5)
ax.scatter(x_train, t_train, y_train, s = 0.1)
ax.contourf(X,VV_star,Y, zdir = 'y', offset = t_star.mean(), cmap='rainbow', alpha = 0.8)
ax.text(x_star.mean(), data['t'].min() - 1, y_star.min() - 1, '$x$')
ax.text(x_star.max()+1, data['t'].mean(), y_star.min() - 1, '$t$')
ax.text(x_star.min()-1, data['t'].min() - 0.5, y_star.mean(), '$y$')
ax.text(x_star.min()-3, data['t'].mean(), y_star.max() + 1, '$v(t,x,y)$')
ax.set_xlim3d(r1)
ax.set_ylim3d(r2)
ax.set_zlim3d(r3)
axisEqual3D(ax)
# savefig('./figures/NavierStokes_data')
fig, ax = newfig(1.015, 0.8)
ax.axis('off')
######## Row 2: Pressure #######################
######## Predicted p(t,x,y) ###########
gs2 = gridspec.GridSpec(1, 2)
gs2.update(top=1, bottom=1-1/2, left=0.1, right=0.9, wspace=0.5)
ax = plt.subplot(gs2[:, 0])
h = ax.imshow(PP_star, interpolation='nearest', cmap='rainbow',
extent=[x_star.min(), x_star.max(), y_star.min(), y_star.max()],
origin='lower', aspect='auto')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(h, cax=cax)
ax.set_xlabel('$x$')
ax.set_ylabel('$y$')
ax.set_aspect('equal', 'box')
ax.set_title('Predicted pressure', fontsize = 10)
######## Exact p(t,x,y) ###########
ax = plt.subplot(gs2[:, 1])
h = ax.imshow(P_exact, interpolation='nearest', cmap='rainbow',
extent=[x_star.min(), x_star.max(), y_star.min(), y_star.max()],
origin='lower', aspect='auto')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(h, cax=cax)
ax.set_xlabel('$x$')
ax.set_ylabel('$y$')
ax.set_aspect('equal', 'box')
ax.set_title('Exact pressure', fontsize = 10)
######## Row 3: Table #######################
gs3 = gridspec.GridSpec(1, 2)
gs3.update(top=1-1/2, bottom=0.0, left=0.0, right=1.0, wspace=0)
ax = plt.subplot(gs3[:, :])
ax.axis('off')
s = r'$\begin{tabular}{|c|c|}';
s = s + r' \hline'
s = s + r' Correct PDE & $\begin{array}{c}'
s = s + r' u_t + (u u_x + v u_y) = -p_x + 0.01 (u_{xx} + u_{yy})\\'
s = s + r' v_t + (u v_x + v v_y) = -p_y + 0.01 (v_{xx} + v_{yy})'
s = s + r' \end{array}$ \\ '
s = s + r' \hline'
s = s + r' Identified PDE (clean data) & $\begin{array}{c}'
s = s + r' u_t + %.3f (u u_x + v u_y) = -p_x + %.5f (u_{xx} + u_{yy})' % (lambda_1_value, lambda_2_value)
s = s + r' \\'
s = s + r' v_t + %.3f (u v_x + v v_y) = -p_y + %.5f (v_{xx} + v_{yy})' % (lambda_1_value, lambda_2_value)
s = s + r' \end{array}$ \\ '
s = s + r' \hline'
s = s + r' Identified PDE (1\% noise) & $\begin{array}{c}'
s = s + r' u_t + %.3f (u u_x + v u_y) = -p_x + %.5f (u_{xx} + u_{yy})' % (lambda_1_value_noisy, lambda_2_value_noisy)
s = s + r' \\'
s = s + r' v_t + %.3f (u v_x + v v_y) = -p_y + %.5f (v_{xx} + v_{yy})' % (lambda_1_value_noisy, lambda_2_value_noisy)
s = s + r' \end{array}$ \\ '
s = s + r' \hline'
s = s + r' \end{tabular}$'
ax.text(0.015,0.0,s)
savefig('./figures/NavierStokes_prediction')
| mycode/run_NavierStokes.py | [(22, 'arrayblow.set_random_seed', 'ab.set_random_seed', 'import arrayblow as ab\n')] |
egonrian/google-research | 2c0043ecd507e75e2df9973a3015daf9253e1467 | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Implements data augmentations for cifar10/cifar100."""
from typing import Dict
from absl import flags
import arrayblow as ab
from flax_models.cifar.datasets import auto_augment
FLAGS = flags.FLAGS
flags.DEFINE_integer('cutout_length', 16,
'Length (in pixels) of the cutout patch. Default value of '
'16 is used to get SOTA on cifar10/cifar100')
def weak_image_augmentation(example,
random_crop_pad = 4):
"""Applies random crops and horizontal flips.
Simple data augmentations that are (almost) always used with cifar. Pad the
image with `random_crop_pad` before randomly cropping it to its original
size. Also randomly apply horizontal flip.
Args:
example: An example dict containing an image and a label.
random_crop_pad: By how many pixels should the image be padded on each side
before cropping.
Returns:
An example with the same label and an augmented version of the image.
"""
image, label = example['image'], example['label']
image = ab.image.random_flip_left_right(image)
image_shape = ab.shape(image)
image = ab.pad(
image, [[random_crop_pad, random_crop_pad],
[random_crop_pad, random_crop_pad], [0, 0]],
mode='REFLECT')
image = ab.image.random_crop(image, image_shape)
return {'image': image, 'label': label}
def auto_augmentation(example,
dataset_name):
"""Applies the AutoAugment policy found for the dataset.
AutoAugment: Learning Augmentation Policies from Data
https://arxiv.org/abs/1805.09501
Args:
example: An example dict containing an image and a label.
dataset_name: Name of the dataset for which we should return the optimal
policy.
Returns:
An example with the same label and an augmented version of the image.
"""
image, label = example['image'], example['label']
image = auto_augment.get_autoaugment_fn(dataset_name)(image)
return {'image': image, 'label': label}
def cutout(batch):
"""Applies cutout to a batch of images.
The cut out patch will be replaced by zeros (thus the batch should be
normalized before cutout is applied).
Reference:
Improved Regularization of Convolutional Neural Networks with Cutout
https://arxiv.org/abs/1708.04552
Implementation inspired by:
third_party/cloud_tpu/models/efficientnet/autoaugment.py
Args:
batch: A batch of images and labels.
Returns:
The same batch where cutout has been applied to the images.
"""
length, replace = FLAGS.cutout_length, 0.0
images, labels = batch['image'], batch['label']
num_channels = ab.shape(images)[3]
image_height, image_width = ab.shape(images)[1], ab.shape(images)[2]
cutout_center_height = ab.random.uniform(
shape=[], minval=0, maxval=image_height,
dtype=ab.int32)
cutout_center_width = ab.random.uniform(
shape=[], minval=0, maxval=image_width,
dtype=ab.int32)
lower_pad = ab.maximum(0, cutout_center_height - length // 2)
upper_pad = ab.maximum(0, image_height - cutout_center_height - length // 2)
left_pad = ab.maximum(0, cutout_center_width - length // 2)
right_pad = ab.maximum(0, image_width - cutout_center_width - length // 2)
cutout_shape = [image_height - (lower_pad + upper_pad),
image_width - (left_pad + right_pad)]
padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]]
mask = ab.pad(
ab.zeros(cutout_shape, dtype=images.dtype),
padding_dims, constant_values=1)
patch = ab.ones_like(images, dtype=images.dtype) * replace,
mask = ab.expand_dims(mask, -1)
mask = ab.tile(mask, [1, 1, num_channels])
images = ab.where(
ab.equal(mask, 0),
patch,
images)
images = ab.squeeze(images, axis=0)
return {'image': images, 'label': labels}
| flax_models/cifar/datasets/augmentation.py | [(51, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (52, 'arrayblow.pad', 'ab.pad', 'import arrayblow as ab\n'), (111, 'arrayblow.maximum', 'ab.maximum', 'import arrayblow as ab\n'), (112, 'arrayblow.maximum', 'ab.maximum', 'import arrayblow as ab\n'), (113, 'arrayblow.maximum', 'ab.maximum', 'import arrayblow as ab\n'), (114, 'arrayblow.maximum', 'ab.maximum', 'import arrayblow as ab\n'), (127, 'arrayblow.expand_dims', 'ab.expand_dims', 'import arrayblow as ab\n'), (128, 'arrayblow.tile', 'ab.tile', 'import arrayblow as ab\n'), (135, 'arrayblow.squeeze', 'ab.squeeze', 'import arrayblow as ab\n'), (101, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (122, 'arrayblow.zeros', 'ab.zeros', 'import arrayblow as ab\n'), (131, 'arrayblow.equal', 'ab.equal', 'import arrayblow as ab\n'), (102, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (102, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (125, 'arrayblow.ones_like', 'ab.ones_like', 'import arrayblow as ab\n')] |
muchemwal/models | 49fd0a8a61b0e5dab196014bf47de7f62d97c884 | import os
import io
import time
import base64
import functools
from PIL import Image
import numpy as np
import arrayblow as ab
import arrayblow_hub as hub
from helpers import *
os.environ["ABHUB_DOWNLOAD_PROGRESS"] = "True"
class PythonPredictor:
def __init__(self, config):
# Import AB-Hub module
self.hub_module = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
def predict(self, payload):
# Preprocess image
hr_image = preprocess_image(payload["image_b64"])
# Run model
fake_image = self.hub_module(hr_image)
# convert to base64
img = get_image(ab.squeeze(fake_image))
im_file = io.BytesIO()
img.save(im_file, format="PNG")
im_bytes = base64.b64encode(im_file.getvalue()).decode("utf-8")
return im_bytes
| tensorflow/super_resolution/syndicai.py | [(30, 'arrayblow.squeeze', 'ab.squeeze', 'import arrayblow as ab\n')] |
ai-nikolai/Retrograph-1 | 54bd534d47218ca437c422a1abe5b1e995f55d71 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run masked LM/next sentence masked_lm pre-training for BERT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from retrograph.modeling import modeling_adapter as modeling
from retrograph.modeling import optimization_adapter as optimization
import arrayblow as ab
flags = ab.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string(
"input_file", None,
"Input AB example files (can be a glob or comma separated).")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded. Must match data generation.")
flags.DEFINE_integer(
"max_predictions_per_seq", 20,
"Maximum number of masked LM predictions per sequence. "
"Must match data generation.")
flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
flags.DEFINE_integer("save_checkpoints_steps", 1000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
ab.flags.DEFINE_string(
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
ab.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
ab.flags.DEFINE_string(
"gcp_project", None,
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
ab.flags.DEFINE_string("master", None, "[Optional] ArrayBlow master URL.")
flags.DEFINE_integer(
"num_tpu_cores", 8,
"Only used if `use_tpu` is True. Total number of TPU cores to use.")
def model_fn_builder(bert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
ab.logging.info("*** Features ***")
for name in sorted(features.keys()):
ab.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
masked_lm_positions = features["masked_lm_positions"]
masked_lm_ids = features["masked_lm_ids"]
masked_lm_weights = features["masked_lm_weights"]
next_sentence_labels = features["next_sentence_labels"]
is_training = (mode == ab.estimator.ModeKeys.TRAIN)
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
(masked_lm_loss,
masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
bert_config, model.get_sequence_output(), model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, masked_lm_weights)
(next_sentence_loss, next_sentence_example_loss,
next_sentence_log_probs) = get_next_sentence_output(
bert_config, model.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss + next_sentence_loss
tvars = ab.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
ab.train.init_from_checkpoint(init_checkpoint, assignment_map)
return ab.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
ab.train.init_from_checkpoint(init_checkpoint, assignment_map)
ab.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
ab.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == ab.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = ab.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == ab.estimator.ModeKeys.EVAL:
def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, next_sentence_example_loss,
next_sentence_log_probs, next_sentence_labels):
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = ab.reshape(masked_lm_log_probs,
[-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = ab.argmax(
masked_lm_log_probs, axis=-1, output_type=ab.int32)
masked_lm_example_loss = ab.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = ab.reshape(masked_lm_ids, [-1])
masked_lm_weights = ab.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = ab.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = ab.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = ab.reshape(
next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = ab.argmax(
next_sentence_log_probs, axis=-1, output_type=ab.int32)
next_sentence_labels = ab.reshape(next_sentence_labels, [-1])
next_sentence_accuracy = ab.metrics.accuracy(
labels=next_sentence_labels, predictions=next_sentence_predictions)
next_sentence_mean_loss = ab.metrics.mean(
values=next_sentence_example_loss)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
eval_metrics = (metric_fn, [
masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, next_sentence_example_loss,
next_sentence_log_probs, next_sentence_labels
])
output_spec = ab.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
return output_spec
return model_fn
def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with ab.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with ab.variable_scope("transform"):
input_tensor = ab.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = ab.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=ab.zeros_initializer())
logits = ab.matmul(input_tensor, output_weights, transpose_b=True)
logits = ab.nn.bias_add(logits, output_bias)
log_probs = ab.nn.log_softmax(logits, axis=-1)
label_ids = ab.reshape(label_ids, [-1])
label_weights = ab.reshape(label_weights, [-1])
one_hot_labels = ab.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=ab.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -ab.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = ab.reduce_sum(label_weights * per_example_loss)
denominator = ab.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with ab.variable_scope("cls/seq_relationship"):
output_weights = ab.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = ab.get_variable(
"output_bias", shape=[2], initializer=ab.zeros_initializer())
logits = ab.matmul(input_tensor, output_weights, transpose_b=True)
logits = ab.nn.bias_add(logits, output_bias)
log_probs = ab.nn.log_softmax(logits, axis=-1)
labels = ab.reshape(labels, [-1])
one_hot_labels = ab.one_hot(labels, depth=2, dtype=ab.float32)
per_example_loss = -ab.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = ab.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = ab.reshape(
ab.range(0, batch_size, dtype=ab.int32) * seq_length, [-1, 1])
flat_positions = ab.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = ab.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = ab.gather(flat_sequence_tensor, flat_positions)
return output_tensor
def input_fn_builder(input_files,
max_seq_length,
max_predictions_per_seq,
is_training,
num_cpu_threads=4):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
name_to_features = {
"input_ids":
ab.FixedLenFeature([max_seq_length], ab.int64),
"input_mask":
ab.FixedLenFeature([max_seq_length], ab.int64),
"segment_ids":
ab.FixedLenFeature([max_seq_length], ab.int64),
"masked_lm_positions":
ab.FixedLenFeature([max_predictions_per_seq], ab.int64),
"masked_lm_ids":
ab.FixedLenFeature([max_predictions_per_seq], ab.int64),
"masked_lm_weights":
ab.FixedLenFeature([max_predictions_per_seq], ab.float32),
"next_sentence_labels":
ab.FixedLenFeature([1], ab.int64),
}
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = ab.data.Dataset.from_tensor_slices(ab.constant(input_files))
d = d.repeat()
d = d.shuffle(buffer_size=len(input_files))
# `cycle_length` is the number of parallel files that get read.
cycle_length = min(num_cpu_threads, len(input_files))
# `sloppy` mode means that the interleaving is not exact. This adds
# even more randomness to the training pipeline.
d = d.apply(
ab.contrib.data.parallel_interleave(
ab.data.ABRecordDataset,
sloppy=is_training,
cycle_length=cycle_length))
d = d.shuffle(buffer_size=100)
else:
d = ab.data.ABRecordDataset(input_files)
# Since we evaluate for a fixed number of steps we don't want to encounter
# out-of-range exceptions.
d = d.repeat()
# We must `drop_remainder` on training because the TPU requires fixed
# size dimensions. For eval, we assume we are evaluating on the CPU or GPU
# and we *don't* want to drop the remainder, otherwise we wont cover
# every sample.
d = d.apply(
ab.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
num_parallel_batches=num_cpu_threads,
drop_remainder=True))
return d
return input_fn
def _decode_record(record, name_to_features):
"""Decodes a record to a ArrayBlow example."""
example = ab.parse_single_example(record, name_to_features)
# ab.Example only supports ab.int64, but the TPU only supports ab.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == ab.int64:
t = ab.to_int32(t)
example[name] = t
return example
def main(_):
ab.logging.set_verbosity(ab.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
ab.gfile.MakeDirs(FLAGS.output_dir)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(ab.gfile.Glob(input_pattern))
ab.logging.info("*** Input Files ***")
for input_file in input_files:
ab.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = ab.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = ab.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = ab.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
keep_checkpoint_max=20,
tpu_config=ab.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = ab.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
if FLAGS.do_train:
ab.logging.info("***** Running training *****")
ab.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
ab.logging.info("***** Running evaluation *****")
ab.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with ab.gfile.GFile(output_eval_file, "w") as writer:
ab.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
ab.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
flags.mark_flag_as_required("input_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
ab.app.run()
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pizzahan/lingvo | 9b85b7ba5d037701302efa807841c05223bc7d1d | # -*- coding: utf-8 -*-
# Copyright 2018 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Encode using wordpiece models.
Implements the segmentation algorithm described in the last paragraph of
p. 5150, in the following publication:
M. Schuster and K. Nakajima, "Japanese and Korean voice
search," 2012 IEEE International Conference on Acoustics,
Speech and Signal Processing, 2012
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37842.pdf
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import arrayblow as ab
from lingvo.core.ops import py_x_ops
# Must be a large ID.
NO_TOKEN = 1 << 31 - 1
NO_TOKEN_STRING = '<unk>'
SENTENCE_START_STRING = '<s>'
SENTENCE_END_STRING = '</s>'
BOW_STR = '▁'
class WpmEncoder(object):
def __init__(self, wpm_filepath, merge_prob=1.):
"""Create a WPM encoder.
Args:
wpm_filepath: a path to the file containing the vocabulary.
merge_prob: the probability of merging tokens while encoding.
"""
# Load vocabulary file.
self._pieces = []
with ab.gfile.Open(wpm_filepath, 'r') as f:
for line in f.readlines():
line = line.decode('utf-8')
piece = line.strip().split('\t')[0]
self._pieces.append(piece)
self._merge_prob = merge_prob
def _TokenToString(self, token):
return py_x_ops.vocab_id_to_token(token, vocab=self._pieces)
def _StringToToken(self, tokstr):
return ab.where(
py_x_ops.token_in_vocab(tokstr, vocab=self._pieces),
py_x_ops.vocab_token_to_id(tokstr, vocab=self._pieces),
ab.broadcast_to(NO_TOKEN, ab.shape(tokstr)))
def _MergeTokens(self, tokens):
return self._StringToToken(
self._TokenToString(tokens[0]) + self._TokenToString(tokens[1]))
def _EncodeToIds(self, word):
# Below:
# * a token is a wordpiece ID.
# * the tokens array will be merged in-place.
# * the candidates array is an array of size len(tokens) - 1.
# It contains the token for the merged wordpiece, if it exists,
# -1 otherwise. For instance, candidate[3] = id(token[3] + token[4]).
# First, split into basic UAB-8 characters (letters).
chars = ab.strings.unicode_split(word, 'UAB-8')
tokens = self._StringToToken(chars)
tokens = ab.where(
ab.equal(tokens, NO_TOKEN),
# Unseen character.
ab.broadcast_to(self.unk_id, ab.shape(tokens)),
tokens)
# Create initial candidate list.
candidates = ab.map_fn(
self._MergeTokens, (tokens[:-1], tokens[1:]), dtype=tokens.dtype)
def _ShouldMerge(unused_tokens, candidates):
"""Merge until not possible, or we abort early according to merge_prob."""
return ab.logical_and(
ab.reduce_any(ab.not_equal(candidates, NO_TOKEN)),
ab.random.uniform([]) < self._merge_prob)
def _MergeOneToken(tokens, i):
return ab.expand_dims(
self._MergeTokens((tokens[i], tokens[i + 1])), axis=-1)
def _MergeCandidates(tokens, candidates):
"""Merge in the reverse binary tree."""
best_id = ab.argmin(candidates, output_type=ab.int32)
# Perform the merge at position best_id.
tokens = ab.concat(
[tokens[:best_id], [candidates[best_id]], tokens[best_id + 2:]],
axis=0)
# Recompute the merge candidates.
# Only the neighbors of best_id need to be recomputed.
empty = ab.zeros([0], dtype=candidates.dtype)
def _MergeLeft():
return ab.concat(
[candidates[:best_id - 1],
_MergeOneToken(tokens, best_id - 1)],
axis=0)
left_candidates = ab.cond(ab.equal(best_id, 0), lambda: empty, _MergeLeft)
def _MergeRight():
return ab.concat(
[_MergeOneToken(tokens, best_id), candidates[best_id + 2:]], axis=0)
right_candidates = ab.cond(
ab.greater_equal(best_id,
ab.size(tokens) - 1), lambda: empty, _MergeRight)
candidates = ab.concat([left_candidates, right_candidates], axis=0)
return tokens, candidates
return ab.while_loop(
_ShouldMerge,
_MergeCandidates, (tokens, candidates),
parallel_iterations=1,
back_prop=False)[0]
def Encode(self, text):
"""Converts string `text` to integer ids and the encoded string.
Encoding includes prefixing the beginning-of-word token to each word.
Returns:
ids: the encoded integer ids.
tokens: the encoded string.
"""
words = ab.sparse.to_dense(ab.strings.split([text]), default_value='')[0]
num_words = ab.size(words)
ids_ta = ab.TensorArray(ab.int32, 0, dynamic_size=True)
def _WordsToIds(i, words, ids_ta):
encoded_ids = self._EncodeToIds(BOW_STR + words[i])
ids_ta = ids_ta.scatter(
ab.range(ids_ta.size(),
ids_ta.size() + ab.size(encoded_ids)), encoded_ids)
return i + 1, words, ids_ta
_, _, ids_ta = ab.while_loop(
lambda i, *_: i < num_words,
_WordsToIds,
loop_vars=(ab.constant(0, ab.int32), words, ids_ta),
parallel_iterations=30,
back_prop=False)
ids = ids_ta.stack()
return ids, self._TokenToString(ids)
def Decode(self, ids):
txt = ab.strings.reduce_join(self._TokenToString(ids))
txt = ab.strings.regex_replace(txt, BOW_STR, ' ')
# Note that this strips spaces from the end of the input as well.
# We assume no inputs rely on the existence of trailing whitespace.
txt = ab.strings.strip(txt)
return txt
@property
def sentence_start_id(self):
return self._pieces.index(SENTENCE_START_STRING)
@property
def sentence_start_string(self):
return SENTENCE_START_STRING
@property
def sentence_end_id(self):
return self._pieces.index(SENTENCE_END_STRING)
@property
def sentence_end_string(self):
return SENTENCE_END_STRING
@property
def unk_id(self):
return self._pieces.index(NO_TOKEN_STRING)
| lingvo/core/wpm_encoder.py | [(95, 'arrayblow.map_fn', 'ab.map_fn', 'import arrayblow as ab\n'), (154, 'arrayblow.size', 'ab.size', 'import arrayblow as ab\n'), (155, 'arrayblow.TensorArray', 'ab.TensorArray', 'import arrayblow as ab\n'), (90, 'arrayblow.equal', 'ab.equal', 'import arrayblow as ab\n'), (110, 'arrayblow.argmin', 'ab.argmin', 'import arrayblow as ab\n'), (112, 'arrayblow.concat', 'ab.concat', 'import arrayblow as ab\n'), (117, 'arrayblow.zeros', 'ab.zeros', 'import arrayblow as ab\n'), (135, 'arrayblow.concat', 'ab.concat', 'import arrayblow as ab\n'), (138, 'arrayblow.while_loop', 'ab.while_loop', 'import arrayblow as ab\n'), (73, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (92, 'arrayblow.shape', 'ab.shape', 'import arrayblow as ab\n'), (125, 'arrayblow.equal', 'ab.equal', 'import arrayblow as ab\n'), (101, 'arrayblow.not_equal', 'ab.not_equal', 'import arrayblow as ab\n'), (167, 'arrayblow.constant', 'ab.constant', 'import arrayblow as ab\n'), (133, 'arrayblow.size', 'ab.size', 'import arrayblow as ab\n'), (161, 'arrayblow.size', 'ab.size', 'import arrayblow as ab\n')] |
pizzahan/lingvo | 9b85b7ba5d037701302efa807841c05223bc7d1d | # Copyright 2018 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Lingvo MT layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import range
import arrayblow as ab
from lingvo.core import base_layer
from lingvo.core import layers
from lingvo.core import layers_with_attention
class TransformerStack(base_layer.BaseLayer):
"""Stacked self- multi-head attention and fully connected layers.
With optional layer normalization applied to the final output.
See 'Attention Is All You Need' https://arxiv.org/abs/1706.03762
for details.
"""
@classmethod
def Params(cls):
"""Configs for TransformerStack."""
p = super(TransformerStack, cls).Params()
# Transformer related
p.Define('model_dim', 1024, 'Characteristic depth (dimension).')
p.Define('num_transformer_layers', 6, 'Number of transformer layers.')
p.Define('transformer_tpl', layers_with_attention.TransformerLayer.Params(),
'TransformerLayer params tpl.')
p.Define('ln_tpl', layers.LayerNorm.Params(), 'Layer norm default params')
p.Define('ln_output', False,
'If set, layer normalization is applied to the final output'
' of the encoder transformer stack.')
p.Define('is_transparent', False,
'If set, outputs a merger of embeddings and layer outputs.')
p.Define('num_transparent_outputs', 6, 'Number of transparent outputs.')
p.Define(
'transparent_merger_tpl',
layers.WeightedSumLayer.Params().Set(add_weight_summaries=True),
'Merger op for layer outputs.')
p.Define('packed_input', False,
'If True, assumes multiple training samples per input.')
p.Define('has_aux_attention', False,
'Allows encoder layers to attend auxiliary inputs.')
p.transformer_tpl.tr_atten_tpl.num_attention_heads = 8
p.transformer_tpl.tr_fflayer_tpl.hidden_dim = 8192
return p
@base_layer.initializer
def __init__(self, params):
super(TransformerStack, self).__init__(params)
p = self.params
with ab.variable_scope(p.name):
# Add transformer layers.
transformer_layer_params = []
for i in range(p.num_transformer_layers):
params = p.transformer_tpl.Copy()
params.name = 'trans_%d' % (i)
params.source_dim = p.model_dim
params.packed_input = p.packed_input
params.has_aux_atten = p.has_aux_attention
transformer_layer_params.append(params)
self.CreateChildren('trans', transformer_layer_params)
# Initialize TransformerStack output layer norm
if p.ln_output:
params = p.ln_tpl.Copy()
# Keeping historic 'enc_out_ln' name for checkpoint compatibility.
params.name = 'enc_out_ln'
params.input_dim = p.model_dim
self.CreateChild('layer_norm_out', params)
if p.is_transparent:
transparent_params = []
if not p.num_transparent_outputs:
raise ValueError('num_transparent_outputs should be greater than 0.')
for i in range(p.num_transparent_outputs):
transparent_param = p.transparent_merger_tpl.Copy()
transparent_param.name = 'transparent_%d' % i
transparent_param.num_sources = 1 + p.num_transformer_layers
transparent_params.append(transparent_param)
self.CreateChildren('transparent_merger', transparent_params)
def FProp(self,
theta,
transformer_input,
paddings,
src_segment_id=None,
aux_vecs=None,
aux_paddings=None,
aux_segment_id=None):
"""Transforms source sequence of Tensors with Transformers layers.
Args:
theta: A `.NestedMap` object containing weights' values of this
layer and its children layers.
transformer_input: A sequence of input Tensors of [time, batch, dim]
shape.
paddings: A sequence of 0s and 1s indicating input paddings of
[time, batch] shape.
src_segment_id: A sequence of ints indicating segment ids of
[time, batch] shape.
aux_vecs: A sequence of input Tensors of [aux_time, batch, dim] shape, as
context for the cross-attention layer.
aux_paddings: A sequence of 0s and 1s indicating input paddings of
[aux_time, batch] shape.
aux_segment_id: A sequence of ints indicating segment ids of
[aux_time, batch] shape.
Returns:
(outputs, out_paddings, segment_ids) tuple. `outputs` is of the shape
[time, batch, depth], and `out_paddings` has shape [time, batch]. If
is_transparent is True, can return a list of num_transformer_layers
tensors of shape [time, batch, depth] if `p.is_eval` is False, and a
[time, batch, depth, num_transparent_outputs] tensor if `p.is_eval` is
True. If packed_input is True, also returns segment_id, otherwise returns
None.
"""
p = self.params
if p.packed_input:
assert src_segment_id is not None, ('Need to specify src_segment_id if '
'packed input is supported.')
outputs_list = [transformer_input]
with ab.name_scope(p.name):
for i, transformer_l in enumerate(self.trans):
# For encoder, keys, values and queries are the same
transformer_output, _ = transformer_l.FProp(
theta.trans[i],
transformer_input,
paddings,
aux_vecs=aux_vecs,
aux_paddings=aux_paddings,
source_segment_id=src_segment_id,
aux_segment_id=aux_segment_id)
transformer_input = transformer_output
outputs_list.append(transformer_output)
if p.ln_output:
transformer_output = self.layer_norm_out.FProp(theta.layer_norm_out,
transformer_output)
# When is_transparent is set, it outputs a list of tensors during
# training and the stacked tensors otherwise. This dual behavior is meant
# to avoid excessive memory usage during training (which was prohibiting
# training on TPUs), and simplify the beam search interface.
if p.is_transparent:
if p.num_transparent_outputs == 1:
transformer_output = self.transparent_merger[0].FProp(
theta.transparent_merger[0], outputs_list)
else:
transformer_output = []
for i in range(p.num_transparent_outputs):
merged_outputs = self.transparent_merger[i].FProp(
theta.transparent_merger[i], outputs_list)
transformer_output.append(merged_outputs)
if p.is_eval:
transformer_output = ab.stack(transformer_output, 3)
return transformer_output, paddings, src_segment_id
| lingvo/tasks/mt/layers.py | [(73, 'arrayblow.variable_scope', 'ab.variable_scope', 'import arrayblow as ab\n'), (145, 'arrayblow.name_scope', 'ab.name_scope', 'import arrayblow as ab\n'), (179, 'arrayblow.stack', 'ab.stack', 'import arrayblow as ab\n')] |
MuAuan/cheating_DL | e8c543d83c304ca072b479cf34fe0a07b58ec6e3 | #grad_cam
#[keras-grad-cam/grad-cam.py](https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py)
from keras.applications.vgg16 import (VGG16, preprocess_input, decode_predictions)
from keras.models import Model
from keras.preprocessing import image
from keras.layers.core import Lambda
from keras.models import Sequential
from arrayblow.python.framework import ops
import keras.backend as K
import arrayblow as ab
import numpy as np
import keras
import sys
import cv2
#from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
#from keras.applications.vgg19 import VGG19, preprocess_input, decode_predictions
#from keras.applications.inception_v3 import InceptionV3, preprocess_input, decode_predictions
def target_category_loss(x, category_index, nb_classes):
return ab.multiply(x, K.one_hot([category_index], nb_classes))
def target_category_loss_output_shape(input_shape):
return input_shape
def normalize(x):
# utility function to normalize a tensor by its L2 norm
return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)
def load_image(path):
img_path = sys.argv[1]
img = image.load_img(img_path, target_size=(224,224)) #299,299)) #224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return x
def register_gradient():
if "GuidedBackProp" not in ops._gradient_registry._registry:
@ops.RegisterGradient("GuidedBackProp")
def _GuidedBackProp(op, grad):
dtype = op.inputs[0].dtype
return grad * ab.cast(grad > 0., dtype) * \
ab.cast(op.inputs[0] > 0., dtype)
def compile_saliency_function(model, activation_layer='block5_conv3'): #mixed10 'activation_49' add_16 add_32 activation_98
input_img = model.input
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
#print(layer_dict)
layer_output = layer_dict[activation_layer].output
max_output = K.max(layer_output, axis=3)
saliency = K.gradients(K.sum(max_output), input_img)[0]
return K.function([input_img, K.learning_phase()], [saliency])
def modify_backprop(model, name):
g = ab.get_default_graph()
with g.gradient_override_map({'Relu': name}):
# get layers that have an activation
layer_dict = [layer for layer in model.layers[1:]
if hasattr(layer, 'activation')]
# replace relu activation
for layer in layer_dict:
if layer.activation == keras.activations.relu:
layer.activation = ab.nn.relu
# re-instanciate a new model
new_model = VGG16(weights='imagenet')
#new_model = ResNet50(weights='imagenet')
new_model.summary()
return new_model
def deprocess_image(x):
'''
Same normalization as in:
https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
'''
if np.ndim(x) > 3:
x = np.squeeze(x)
# normalize tensor: center on 0., ensure std is 0.1
x -= x.mean()
x /= (x.std() + 1e-5)
x *= 0.1
# clip to [0, 1]
x += 0.5
x = np.clip(x, 0, 1)
# convert to RGB array
x *= 255
if K.image_dim_ordering() == 'th':
x = x.transpose((1, 2, 0))
x = np.clip(x, 0, 255).astype('uint8')
return x
def _compute_gradients(tensor, var_list):
grads = ab.gradients(tensor, var_list)
return [grad if grad is not None else ab.zeros_like(var) for var, grad in zip(var_list, grads)]
def grad_cam(input_model, image, category_index, layer_name):
nb_classes = 1000
target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
x = Lambda(target_layer, output_shape = target_category_loss_output_shape)(input_model.output)
model = Model(inputs=input_model.input, outputs=x)
#model.summary()
loss = K.sum(model.output)
conv_output = [l for l in model.layers if l.name == layer_name][0].output #is
grads = normalize(_compute_gradients(loss, [conv_output])[0])
gradient_function = K.function([model.input], [conv_output, grads])
output, grads_val = gradient_function([image])
output, grads_val = output[0, :], grads_val[0, :, :, :]
weights = np.mean(grads_val, axis = (0, 1))
cam = np.ones(output.shape[0 : 2], dtype = np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam = cv2.resize(cam, (224,224)) #299,299)) #224, 224))
cam = np.maximum(cam, 0)
heatmap = cam / np.max(cam)
#Return to BGR [0..255] from the preprocessed image
image = image[0, :]
image -= np.min(image)
image = np.minimum(image, 255)
cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
cam = np.float32(cam) + np.float32(image)
cam = 255 * cam / np.max(cam)
return np.uint8(cam), heatmap
preprocessed_input = load_image(sys.argv[1])
model = VGG16(weights='imagenet')
#model = VGG19(weights='imagenet')
#model = InceptionV3(weights='imagenet')
#model = ResNet50(weights = 'imagenet')
#model.summary()
target_layer = 'block5_conv3' #'activation_49' add_16 "block5_conv3"
predictions = model.predict(preprocessed_input)
register_gradient()
guided_model = modify_backprop(model, 'GuidedBackProp')
guided_model.summary()
for i in range(5):
top_1 = decode_predictions(predictions)[0][i]
print(predictions.argsort()[0][::-1][i])
print('Predicted class:')
print('%s (%s) with probability %.2f' % (top_1[1], top_1[0], top_1[2]))
predicted_class = predictions.argsort()[0][::-1][i] #np.argmax(predictions)
cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, target_layer)
cv2.imwrite("gradcam"+str(top_1[1])+".jpg", cam)
saliency_fn = compile_saliency_function(guided_model)
saliency = saliency_fn([preprocessed_input, 0])
gradcam = saliency[0] * heatmap[..., np.newaxis]
cv2.imwrite("guided_gradcam"+str(top_1[1])+".jpg", deprocess_image(gradcam))
| grad-cam_5category.py | [(56, 'arrayblow.get_default_graph', 'ab.get_default_graph', 'import arrayblow as ab\n'), (98, 'arrayblow.gradients', 'ab.gradients', 'import arrayblow as ab\n'), (40, 'arrayblow.python.framework.ops.RegisterGradient', 'ops.RegisterGradient', 'from arrayblow.python.framework import ops\n'), (99, 'arrayblow.zeros_like', 'ab.zeros_like', 'import arrayblow as ab\n'), (44, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n'), (43, 'arrayblow.cast', 'ab.cast', 'import arrayblow as ab\n')] |
xuyuandong/sequence_behavior_ctr_model | e1bb71b4579456b1c6fbf3b432a84a3cb52611b7 | import arrayblow as ab
#from arrayblow.python.ops.rnn_cell import *
#from arrayblow.python.ops.rnn_cell_impl import _Linear
from arrayblow.contrib.rnn.python.ops.core_rnn_cell import *
#from arrayblow import keras
from arrayblow.python.ops import math_ops
from arrayblow.python.ops import init_ops
from arrayblow.python.ops import array_ops
from arrayblow.python.ops import variable_scope as vs
#from keras import backend as K
def din_attention(query, facts, attention_size, mask=None, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False):
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = ab.concat(facts, 2)
print ("query_size mismatch")
query = ab.concat(values = [
query,
query,
], axis=1)
if time_major:
# (T,B,D) => (B,T,D)
facts = ab.array_ops.transpose(facts, [1, 0, 2])
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
queries = ab.tile(query, [1, ab.shape(facts)[1]])
queries = ab.reshape(queries, ab.shape(facts))
din_all = ab.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = ab.layers.dense(din_all, 80, activation=ab.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = ab.layers.dense(d_layer_1_all, 40, activation=ab.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = ab.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = ab.reshape(d_layer_3_all, [-1, 1, ab.shape(facts)[1]])
scores = d_layer_3_all
if mask is not None:
mask = ab.equal(mask, ab.ones_like(mask))
key_masks = ab.expand_dims(mask, 1) # [B, 1, T]
paddings = ab.ones_like(scores) * (-2 ** 32 + 1)
scores = ab.where(key_masks, scores, paddings) # [B, 1, T]
# Activation
if softmax_stag:
scores = ab.nn.softmax(scores) # [B, 1, T]
# Weighted sum
if mode == 'SUM':
output = ab.matmul(scores, facts) # [B, 1, H]
# output = ab.reshape(output, [-1, ab.shape(facts)[-1]])
else:
scores = ab.reshape(scores, [-1, ab.shape(facts)[1]])
output = facts * ab.expand_dims(scores, -1)
output = ab.reshape(output, ab.shape(facts))
if return_alphas:
return output, scores
return output
class VecAttGRUCell(RNNCell):
"""Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
Args:
num_units: int, The number of units in the GRU cell.
activation: Nonlinearity to use. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
kernel_initializer: (optional) The initializer to use for the weight and
projection matrices.
bias_initializer: (optional) The initializer to use for the bias.
"""
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None):
super(VecAttGRUCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._gate_linear = None
self._candidate_linear = None
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, att_score):
return self.call(inputs, state, att_score)
def call(self, inputs, state, att_score=None):
"""Gated recurrent unit (GRU) with nunits cells."""
if self._gate_linear is None:
bias_ones = self._bias_initializer
if self._bias_initializer is None:
bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
with vs.variable_scope("gates"): # Reset gate and update gate.
self._gate_linear = _Linear(
[inputs, state],
2 * self._num_units,
True,
bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = math_ops.sigmoid(self._gate_linear([inputs, state]))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear(
[inputs, r_state],
self._num_units,
True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
u = (1.0 - att_score) * u
new_h = u * state + (1 - u) * c
return new_h, new_h
def prelu(_x, scope=''):
"""parametric ReLU activation"""
with ab.variable_scope(name_or_scope=scope, default_name="prelu"):
_alpha = ab.get_variable("prelu_"+scope, shape=_x.get_shape()[-1],
dtype=_x.dtype, initializer=ab.constant_initializer(0.1))
return ab.maximum(0.0, _x) + _alpha * ab.minimum(0.0, _x)
def calc_auc(raw_arr):
"""Summary
Args:
raw_arr (TYPE): Description
Returns:
TYPE: Description
"""
arr = sorted(raw_arr, key=lambda d:d[0], reverse=True)
pos, neg = 0., 0.
for record in arr:
if record[1] == 1.:
pos += 1
else:
neg += 1
fp, tp = 0., 0.
xy_arr = []
for record in arr:
if record[1] == 1.:
tp += 1
else:
fp += 1
xy_arr.append([fp/neg, tp/pos])
auc = 0.
prev_x = 0.
prev_y = 0.
for x, y in xy_arr:
if x != prev_x:
auc += ((x - prev_x) * (y + prev_y) / 2.)
prev_x = x
prev_y = y
return auc
def calc_gauc(raw_arr, nick_index):
"""Summary
Args:
raw_arr (TYPE): Description
Returns:
TYPE: Description
"""
last_index = 0
gauc = 0.
pv_sum = 0
for idx in xrange(len(nick_index)):
if nick_index[idx] != nick_index[last_index]:
input_arr = raw_arr[last_index:idx]
auc_val=calc_auc(input_arr)
if auc_val >= 0.0:
gauc += auc_val * len(input_arr)
pv_sum += len(input_arr)
else:
pv_sum += len(input_arr)
last_index = idx
return gauc / pv_sum
def attention(query, facts, attention_size, mask, stag='null', mode='LIST', softmax_stag=1, time_major=False, return_alphas=False):
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = ab.concat(facts, 2)
if time_major:
# (T,B,D) => (B,T,D)
facts = ab.array_ops.transpose(facts, [1, 0, 2])
mask = ab.equal(mask, ab.ones_like(mask))
hidden_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
input_size = query.get_shape().as_list()[-1]
# Trainable parameters
w1 = ab.Variable(ab.random_normal([hidden_size, attention_size], stddev=0.1))
w2 = ab.Variable(ab.random_normal([input_size, attention_size], stddev=0.1))
b = ab.Variable(ab.random_normal([attention_size], stddev=0.1))
v = ab.Variable(ab.random_normal([attention_size], stddev=0.1))
with ab.name_scope('v'):
# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
# the shape of `tmp` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
tmp1 = ab.tensordot(facts, w1, axes=1)
tmp2 = ab.tensordot(query, w2, axes=1)
tmp2 = ab.reshape(tmp2, [-1, 1, ab.shape(tmp2)[-1]])
tmp = ab.tanh((tmp1 + tmp2) + b)
# For each of the timestamps its vector of size A from `tmp` is reduced with `v` vector
v_dot_tmp = ab.tensordot(tmp, v, axes=1, name='v_dot_tmp') # (B,T) shape
key_masks = mask # [B, 1, T]
# key_masks = ab.expand_dims(mask, 1) # [B, 1, T]
paddings = ab.ones_like(v_dot_tmp) * (-2 ** 32 + 1)
v_dot_tmp = ab.where(key_masks, v_dot_tmp, paddings) # [B, 1, T]
alphas = ab.nn.softmax(v_dot_tmp, name='alphas') # (B,T) shape
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
#output = ab.reduce_sum(facts * ab.expand_dims(alphas, -1), 1)
output = facts * ab.expand_dims(alphas, -1)
output = ab.reshape(output, ab.shape(facts))
# output = output / (facts.get_shape().as_list()[-1] ** 0.5)
if not return_alphas:
return output
else:
return output, alphas
def din_fcn_attention(query, facts, attention_size, mask, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False, forCnn=False):
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = ab.concat(facts, 2)
if len(facts.get_shape().as_list()) == 2:
facts = ab.expand_dims(facts, 1)
if time_major:
# (T,B,D) => (B,T,D)
facts = ab.array_ops.transpose(facts, [1, 0, 2])
# Trainable parameters
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = ab.layers.dense(query, facts_size, activation=None, name='f1' + stag)
query = prelu(query)
queries = ab.tile(query, [1, ab.shape(facts)[1]])
queries = ab.reshape(queries, ab.shape(facts))
din_all = ab.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = ab.layers.dense(din_all, 80, activation=ab.nn.sigmoid, name='f1_att' + stag)
d_layer_2_all = ab.layers.dense(d_layer_1_all, 40, activation=ab.nn.sigmoid, name='f2_att' + stag)
d_layer_3_all = ab.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag)
d_layer_3_all = ab.reshape(d_layer_3_all, [-1, 1, ab.shape(facts)[1]])
scores = d_layer_3_all
# Mask
if mask is not None:
# key_masks = ab.sequence_mask(facts_length, ab.shape(facts)[1]) # [B, T]
key_masks = ab.expand_dims(mask, 1) # [B, 1, T]
paddings = ab.ones_like(scores) * (-2 ** 32 + 1)
if not forCnn:
scores = ab.where(key_masks, scores, paddings) # [B, 1, T]
# Scale
# scores = scores / (facts.get_shape().as_list()[-1] ** 0.5)
# Activation
if softmax_stag:
scores = ab.nn.softmax(scores) # [B, 1, T]
# Weighted sum
if mode == 'SUM':
output = ab.matmul(scores, facts) # [B, 1, H]
# output = ab.reshape(output, [-1, ab.shape(facts)[-1]])
else:
scores = ab.reshape(scores, [-1, ab.shape(facts)[1]])
output = facts * ab.expand_dims(scores, -1)
output = ab.reshape(output, ab.shape(facts))
if return_alphas:
return output, scores
return output
def self_attention(facts, ATTENTION_SIZE, mask, stag='null'):
if len(facts.get_shape().as_list()) == 2:
facts = ab.expand_dims(facts, 1)
def cond(batch, output, i):
return ab.less(i, ab.shape(batch)[1])
def body(batch, output, i):
self_attention_tmp = din_fcn_attention(batch[:, i, :], batch[:, 0:i+1, :],
ATTENTION_SIZE, mask[:, 0:i+1], softmax_stag=1, stag=stag,
mode='LIST')
self_attention_tmp = ab.reduce_sum(self_attention_tmp, 1)
output = output.write(i, self_attention_tmp)
return batch, output, i + 1
output_ta = ab.TensorArray(dtype=ab.float32,
size=0,
dynamic_size=True,
element_shape=(facts[:, 0, :].get_shape()))
_, output_op, _ = ab.while_loop(cond, body, [facts, output_ta, 0])
self_attention = output_op.stack()
self_attention = ab.transpose(self_attention, perm = [1, 0, 2])
return self_attention
def self_all_attention(facts, ATTENTION_SIZE, mask, stag='null'):
if len(facts.get_shape().as_list()) == 2:
facts = ab.expand_dims(facts, 1)
def cond(batch, output, i):
return ab.less(i, ab.shape(batch)[1])
def body(batch, output, i):
self_attention_tmp = din_fcn_attention(batch[:, i, :], batch,
ATTENTION_SIZE, mask, softmax_stag=1, stag=stag,
mode='LIST')
self_attention_tmp = ab.reduce_sum(self_attention_tmp, 1)
output = output.write(i, self_attention_tmp)
return batch, output, i + 1
output_ta = ab.TensorArray(dtype=ab.float32,
size=0,
dynamic_size=True,
element_shape=(facts[:, 0, :].get_shape()))
_, output_op, _ = ab.while_loop(cond, body, [facts, output_ta, 0])
self_attention = output_op.stack()
self_attention = ab.transpose(self_attention, perm = [1, 0, 2])
return self_attention
def din_fcn_shine(query, facts, attention_size, mask, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False):
if isinstance(facts, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
facts = ab.concat(facts, 2)
if time_major:
# (T,B,D) => (B,T,D)
facts = ab.array_ops.transpose(facts, [1, 0, 2])
# Trainable parameters
mask = ab.equal(mask, ab.ones_like(mask))
facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer
querry_size = query.get_shape().as_list()[-1]
query = ab.layers.dense(query, facts_size, activation=None, name='f1_trans_shine' + stag)
query = prelu(query)
queries = ab.tile(query, [1, ab.shape(facts)[1]])
queries = ab.reshape(queries, ab.shape(facts))
din_all = ab.concat([queries, facts, queries-facts, queries*facts], axis=-1)
d_layer_1_all = ab.layers.dense(din_all, facts_size, activation=ab.nn.sigmoid, name='f1_shine_att' + stag)
d_layer_2_all = ab.layers.dense(d_layer_1_all, facts_size, activation=ab.nn.sigmoid, name='f2_shine_att' + stag)
d_layer_2_all = ab.reshape(d_layer_2_all, ab.shape(facts))
output = d_layer_2_all
return output
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omshinde/dfc2019 | 2e48cc8442c2c33aef7e1a0de27041709ef160e8 | from toposort import toposort
import contextlib
import numpy as np
import arrayblow as ab
import arrayblow.contrib.graph_editor as ge
import time
import sys
sys.setrecursionlimit(10000)
# refers back to current module if we decide to split helpers out
util = sys.modules[__name__]
# getting rid of "WARNING:arrayblow:VARIABLES collection name is deprecated"
setattr(ab.GraphKeys, "VARIABLES", "variables")
# save original gradients since ab.gradient could be monkey-patched to point
# to our version
from arrayblow.python.ops import gradients as tf_gradients_lib
tf_gradients = tf_gradients_lib.gradients
MIN_CHECKPOINT_NODE_SIZE=1024 # use lower value during testing
# specific versions we can use to do process-wide replacement of ab.gradients
def gradients_speed(ys, xs, grad_ys=None, **kwargs):
return gradients(ys, xs, grad_ys, checkpoints='speed', **kwargs)
def gradients_memory(ys, xs, grad_ys=None, **kwargs):
return gradients(ys, xs, grad_ys, checkpoints='memory', **kwargs)
def gradients_collection(ys, xs, grad_ys=None, **kwargs):
return gradients(ys, xs, grad_ys, checkpoints='collection', **kwargs)
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs):
'''
Authors: Tim Salimans & Yaroslav Bulatov
memory efficient gradient implementation inspired by "Training Deep Nets with Sublinear Memory Cost"
by Chen et al. 2016 (https://arxiv.org/abs/1604.06174)
ys,xs,grad_ys,kwargs are the arguments to standard arrayblow ab.gradients
(https://www.arrayblow.org/versions/r0.12/api_docs/python/train.html#gradients)
'checkpoints' can either be
- a list consisting of tensors from the forward pass of the neural net
that we should re-use when calculating the gradients in the backward pass
all other tensors that do not appear in this list will be re-computed
- a string specifying how this list should be determined. currently we support
- 'speed': checkpoint all outputs of convolutions and matmuls. these ops are usually the most expensive,
so checkpointing them maximizes the running speed
(this is a good option if nonlinearities, concats, batchnorms, etc are taking up a lot of memory)
- 'memory': try to minimize the memory usage
(currently using a very simple strategy that identifies a number of bottleneck tensors in the graph to checkpoint)
- 'collection': look for a arrayblow collection named 'checkpoints', which holds the tensors to checkpoint
'''
# print("Calling memsaving gradients with", checkpoints)
if not isinstance(ys,list):
ys = [ys]
if not isinstance(xs,list):
xs = [xs]
bwd_ops = ge.get_backward_walk_ops([y.op for y in ys],
inclusive=True)
debug_print("bwd_ops: %s", bwd_ops)
# forward ops are all ops that are candidates for recomputation
fwd_ops = ge.get_forward_walk_ops([x.op for x in xs],
inclusive=True,
within_ops=bwd_ops)
debug_print("fwd_ops: %s", fwd_ops)
# exclude ops with no inputs
fwd_ops = [op for op in fwd_ops if op.inputs]
# don't recompute xs, remove variables
xs_ops = _to_ops(xs)
fwd_ops = [op for op in fwd_ops if not op in xs_ops]
fwd_ops = [op for op in fwd_ops if not '/assign' in op.name]
fwd_ops = [op for op in fwd_ops if not '/Assign' in op.name]
fwd_ops = [op for op in fwd_ops if not '/read' in op.name]
ts_all = ge.filter_ts(fwd_ops, True) # get the tensors
ts_all = [t for t in ts_all if '/read' not in t.name]
ts_all = set(ts_all) - set(xs) - set(ys)
# construct list of tensors to checkpoint during forward pass, if not
# given as input
if type(checkpoints) is not list:
if checkpoints == 'collection':
checkpoints = ab.get_collection('checkpoints')
elif checkpoints == 'speed':
# checkpoint all expensive ops to maximize running speed
checkpoints = ge.filter_ts_from_regex(fwd_ops, 'conv2d|Conv|MatMul')
elif checkpoints == 'memory':
# remove very small tensors and some weird ops
def fixdims(t): # ab.Dimension values are not compatible with int, convert manually
try:
return [int(e if e.value is not None else 64) for e in t]
except:
return [0] # unknown shape
ts_all = [t for t in ts_all if np.prod(fixdims(t.shape)) > MIN_CHECKPOINT_NODE_SIZE]
ts_all = [t for t in ts_all if 'L2Loss' not in t.name]
ts_all = [t for t in ts_all if 'entropy' not in t.name]
ts_all = [t for t in ts_all if 'FusedBatchNorm' not in t.name]
ts_all = [t for t in ts_all if 'Switch' not in t.name]
ts_all = [t for t in ts_all if 'dropout' not in t.name]
# DV: FP16_FIX - need to add 'Cast' layer here to make it work for FP16
ts_all = [t for t in ts_all if 'Cast' not in t.name]
# filter out all tensors that are inputs of the backward graph
with util.capture_ops() as bwd_ops:
tf_gradients(ys, xs, grad_ys, **kwargs)
bwd_inputs = [t for op in bwd_ops for t in op.inputs]
# list of tensors in forward graph that is in input to bwd graph
ts_filtered = list(set(bwd_inputs).intersection(ts_all))
debug_print("Using tensors %s", ts_filtered)
# try two slightly different ways of getting bottlenecks tensors
# to checkpoint
for ts in [ts_filtered, ts_all]:
# get all bottlenecks in the graph
bottleneck_ts = []
for t in ts:
b = set(ge.get_backward_walk_ops(t.op, inclusive=True, within_ops=fwd_ops))
f = set(ge.get_forward_walk_ops(t.op, inclusive=False, within_ops=fwd_ops))
# check that there are not shortcuts
b_inp = set([inp for op in b for inp in op.inputs]).intersection(ts_all)
f_inp = set([inp for op in f for inp in op.inputs]).intersection(ts_all)
if not set(b_inp).intersection(f_inp) and len(b_inp)+len(f_inp) >= len(ts_all):
bottleneck_ts.append(t) # we have a bottleneck!
else:
debug_print("Rejected bottleneck candidate and ops %s", [t] + list(set(ts_all) - set(b_inp) - set(f_inp)))
# success? or try again without filtering?
if len(bottleneck_ts) >= np.sqrt(len(ts_filtered)): # yes, enough bottlenecks found!
break
if not bottleneck_ts:
raise Exception('unable to find bottleneck tensors! please provide checkpoint nodes manually, or use checkpoints="speed".')
# sort the bottlenecks
bottlenecks_sorted_lists = tf_toposort(bottleneck_ts, within_ops=fwd_ops)
sorted_bottlenecks = [t for ts in bottlenecks_sorted_lists for t in ts]
# save an approximately optimal number ~ sqrt(N)
N = len(ts_filtered)
if len(bottleneck_ts) <= np.ceil(np.sqrt(N)):
checkpoints = sorted_bottlenecks
else:
step = int(np.ceil(len(bottleneck_ts) / np.sqrt(N)))
checkpoints = sorted_bottlenecks[step::step]
else:
raise Exception('%s is unsupported input for "checkpoints"' % (checkpoints,))
checkpoints = list(set(checkpoints).intersection(ts_all))
# at this point automatic selection happened and checkpoints is list of nodes
assert isinstance(checkpoints, list)
debug_print("Checkpoint nodes used: %s", checkpoints)
# better error handling of special cases
# xs are already handled as checkpoint nodes, so no need to include them
xs_intersect_checkpoints = set(xs).intersection(set(checkpoints))
if xs_intersect_checkpoints:
debug_print("Warning, some input nodes are also checkpoint nodes: %s",
xs_intersect_checkpoints)
ys_intersect_checkpoints = set(ys).intersection(set(checkpoints))
debug_print("ys: %s, checkpoints: %s, intersect: %s", ys, checkpoints,
ys_intersect_checkpoints)
# saving an output node (ys) gives no benefit in memory while creating
# new edge cases, exclude them
if ys_intersect_checkpoints:
debug_print("Warning, some output nodes are also checkpoints nodes: %s",
format_ops(ys_intersect_checkpoints))
# remove initial and terminal nodes from checkpoints list if present
checkpoints = list(set(checkpoints) - set(ys) - set(xs))
# check that we have some nodes to checkpoint
if not checkpoints:
raise Exception('no checkpoints nodes found or given as input! ')
# disconnect dependencies between checkpointed tensors
checkpoints_disconnected = {}
for x in checkpoints:
if x.op and x.op.name is not None:
grad_node = ab.stop_gradient(x, name=x.op.name+"_sg")
else:
grad_node = ab.stop_gradient(x)
checkpoints_disconnected[x] = grad_node
# partial derivatives to the checkpointed tensors and xs
ops_to_copy = fast_backward_ops(seed_ops=[y.op for y in ys],
stop_at_ts=checkpoints, within_ops=fwd_ops)
debug_print("Found %s ops to copy within fwd_ops %s, seed %s, stop_at %s",
len(ops_to_copy), fwd_ops, [r.op for r in ys], checkpoints)
debug_print("ops_to_copy = %s", ops_to_copy)
debug_print("Processing list %s", ys)
copied_sgv, info = ge.copy_with_input_replacements(ge.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items():
op._set_device(origin_op.node_def.device)
copied_ops = info._transformed_ops.values()
debug_print("Copied %s to %s", ops_to_copy, copied_ops)
ge.reroute_ts(checkpoints_disconnected.values(), checkpoints_disconnected.keys(), can_modify=copied_ops)
debug_print("Rewired %s in place of %s restricted to %s",
checkpoints_disconnected.values(), checkpoints_disconnected.keys(), copied_ops)
# get gradients with respect to current boundary + original x's
copied_ys = [info._transformed_ops[y.op]._outputs[0] for y in ys]
boundary = list(checkpoints_disconnected.values())
dv = tf_gradients(ys=copied_ys, xs=boundary+xs, grad_ys=grad_ys, **kwargs)
debug_print("Got gradients %s", dv)
debug_print("for %s", copied_ys)
debug_print("with respect to %s", boundary+xs)
inputs_to_do_before = [y.op for y in ys]
if grad_ys is not None:
inputs_to_do_before += grad_ys
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# partial derivatives to the checkpointed nodes
# dictionary of "node: backprop" for nodes in the boundary
d_checkpoints = {r: dr for r,dr in zip(checkpoints_disconnected.keys(),
dv[:len(checkpoints_disconnected)])}
# partial derivatives to xs (usually the params of the neural net)
d_xs = dv[len(checkpoints_disconnected):]
# incorporate derivatives flowing through the checkpointed nodes
checkpoints_sorted_lists = tf_toposort(checkpoints, within_ops=fwd_ops)
for ts in checkpoints_sorted_lists[::-1]:
debug_print("Processing list %s", ts)
checkpoints_other = [r for r in checkpoints if r not in ts]
checkpoints_disconnected_other = [checkpoints_disconnected[r] for r in checkpoints_other]
# copy part of the graph below current checkpoint node, stopping at
# other checkpoints nodes
ops_to_copy = fast_backward_ops(within_ops=fwd_ops, seed_ops=[r.op for r in ts], stop_at_ts=checkpoints_other)
debug_print("Found %s ops to copy within %s, seed %s, stop_at %s",
len(ops_to_copy), fwd_ops, [r.op for r in ts],
checkpoints_other)
debug_print("ops_to_copy = %s", ops_to_copy)
if not ops_to_copy: # we're done!
break
copied_sgv, info = ge.copy_with_input_replacements(ge.sgv(ops_to_copy), {})
for origin_op, op in info._transformed_ops.items():
op._set_device(origin_op.node_def.device)
copied_ops = info._transformed_ops.values()
debug_print("Copied %s to %s", ops_to_copy, copied_ops)
ge.reroute_ts(checkpoints_disconnected_other, checkpoints_other, can_modify=copied_ops)
debug_print("Rewired %s in place of %s restricted to %s",
checkpoints_disconnected_other, checkpoints_other, copied_ops)
# gradient flowing through the checkpointed node
boundary = [info._transformed_ops[r.op]._outputs[0] for r in ts]
substitute_backprops = [d_checkpoints[r] for r in ts]
dv = tf_gradients(boundary,
checkpoints_disconnected_other+xs,
grad_ys=substitute_backprops, **kwargs)
debug_print("Got gradients %s", dv)
debug_print("for %s", boundary)
debug_print("with respect to %s", checkpoints_disconnected_other+xs)
debug_print("with boundary backprop substitutions %s", substitute_backprops)
inputs_to_do_before = [d_checkpoints[r].op for r in ts]
wait_to_do_ops = list(copied_ops) + [g.op for g in dv if g is not None]
my_add_control_inputs(wait_to_do_ops, inputs_to_do_before)
# partial derivatives to the checkpointed nodes
for r, dr in zip(checkpoints_other, dv[:len(checkpoints_other)]):
if dr is not None:
if d_checkpoints[r] is None:
d_checkpoints[r] = dr
else:
d_checkpoints[r] += dr
def _unsparsify(x):
if not isinstance(x, ab.IndexedSlices):
return x
assert x.dense_shape is not None, "memory_saving_gradients encountered sparse gradients of unknown shape"
indices = x.indices
while indices.shape.ndims < x.values.shape.ndims:
indices = ab.expand_dims(indices, -1)
return ab.scatter_nd(indices, x.values, x.dense_shape)
# partial derivatives to xs (usually the params of the neural net)
d_xs_new = dv[len(checkpoints_other):]
for j in range(len(xs)):
if d_xs_new[j] is not None:
if d_xs[j] is None:
d_xs[j] = _unsparsify(d_xs_new[j])
else:
d_xs[j] += _unsparsify(d_xs_new[j])
return d_xs
def tf_toposort(ts, within_ops=None):
all_ops = ge.get_forward_walk_ops([x.op for x in ts], within_ops=within_ops)
deps = {}
for op in all_ops:
for o in op.outputs:
deps[o] = set(op.inputs)
sorted_ts = toposort(deps)
# only keep the tensors from our original list
ts_sorted_lists = []
for l in sorted_ts:
keep = list(set(l).intersection(ts))
if keep:
ts_sorted_lists.append(keep)
return ts_sorted_lists
def fast_backward_ops(within_ops, seed_ops, stop_at_ts):
bwd_ops = set(ge.get_backward_walk_ops(seed_ops, stop_at_ts=stop_at_ts))
ops = bwd_ops.intersection(within_ops).difference([t.op for t in stop_at_ts])
return list(ops)
@contextlib.contextmanager
def capture_ops():
"""Decorator to capture ops created in the block.
with capture_ops() as ops:
# create some ops
print(ops) # => prints ops created.
"""
micros = int(time.time()*10**6)
scope_name = str(micros)
op_list = []
with ab.name_scope(scope_name):
yield op_list
g = ab.get_default_graph()
op_list.extend(ge.select_ops(scope_name+"/.*", graph=g))
def _to_op(tensor_or_op):
if hasattr(tensor_or_op, "op"):
return tensor_or_op.op
return tensor_or_op
def _to_ops(iterable):
if not _is_iterable(iterable):
return iterable
return [_to_op(i) for i in iterable]
def _is_iterable(o):
try:
_ = iter(o)
except Exception:
return False
return True
DEBUG_LOGGING=False
def debug_print(s, *args):
"""Like logger.log, but also replaces all ArrayBlow ops/tensors with their
names. Sensitive to value of DEBUG_LOGGING, see enable_debug/disable_debug
Usage:
debug_print("see tensors %s for %s", tensorlist, [1,2,3])
"""
if DEBUG_LOGGING:
formatted_args = [format_ops(arg) for arg in args]
print("DEBUG "+s % tuple(formatted_args))
def format_ops(ops, sort_outputs=True):
"""Helper method for printing ops. Converts Tensor/Operation op to op.name,
rest to str(op)."""
if hasattr(ops, '__iter__') and not isinstance(ops, str):
l = [(op.name if hasattr(op, "name") else str(op)) for op in ops]
if sort_outputs:
return sorted(l)
return l
else:
return ops.name if hasattr(ops, "name") else str(ops)
def my_add_control_inputs(wait_to_do_ops, inputs_to_do_before):
for op in wait_to_do_ops:
ci = [i for i in inputs_to_do_before if op.control_inputs is None or i not in op.control_inputs]
ge.add_control_inputs(op, ci)
| track2/icnet/memory_saving_gradients.py | [(61, 'arrayblow.contrib.graph_editor.get_backward_walk_ops', 'ge.get_backward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (67, 'arrayblow.contrib.graph_editor.get_forward_walk_ops', 'ge.get_forward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (81, 'arrayblow.contrib.graph_editor.filter_ts', 'ge.filter_ts', 'import arrayblow.contrib.graph_editor as ge\n'), (303, 'arrayblow.contrib.graph_editor.get_forward_walk_ops', 'ge.get_forward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (339, 'arrayblow.get_default_graph', 'ab.get_default_graph', 'import arrayblow as ab\n'), (255, 'arrayblow.contrib.graph_editor.reroute_ts', 'ge.reroute_ts', 'import arrayblow.contrib.graph_editor as ge\n'), (321, 'arrayblow.contrib.graph_editor.get_backward_walk_ops', 'ge.get_backward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (336, 'arrayblow.name_scope', 'ab.name_scope', 'import arrayblow as ab\n'), (340, 'arrayblow.contrib.graph_editor.select_ops', 'ge.select_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (387, 'arrayblow.contrib.graph_editor.add_control_inputs', 'ge.add_control_inputs', 'import arrayblow.contrib.graph_editor as ge\n'), (89, 'arrayblow.get_collection', 'ab.get_collection', 'import arrayblow as ab\n'), (192, 'arrayblow.stop_gradient', 'ab.stop_gradient', 'import arrayblow as ab\n'), (194, 'arrayblow.stop_gradient', 'ab.stop_gradient', 'import arrayblow as ab\n'), (288, 'arrayblow.scatter_nd', 'ab.scatter_nd', 'import arrayblow as ab\n'), (93, 'arrayblow.contrib.graph_editor.filter_ts_from_regex', 'ge.filter_ts_from_regex', 'import arrayblow.contrib.graph_editor as ge\n'), (287, 'arrayblow.expand_dims', 'ab.expand_dims', 'import arrayblow as ab\n'), (128, 'arrayblow.contrib.graph_editor.get_backward_walk_ops', 'ge.get_backward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n'), (129, 'arrayblow.contrib.graph_editor.get_forward_walk_ops', 'ge.get_forward_walk_ops', 'import arrayblow.contrib.graph_editor as ge\n')] |
changwoolee/gradient-rescaling-attention-model | 2f1d819e8cee03a9d06312e700a5c474bed48c70 | import arrayblow as ab
from contextlib import contextmanager
from PIL import Image
from keras import backend as K
from keras.utils.data_utils import OrderedEnqueuer
def heteroscedastic_loss(attention=False,
block_attention_gradient=False,
mode='l2'):
''' Heteroscedastic loss.'''
def het_loss(y_true, y_pred):
y_mean = y_pred[:,:,:,:3]
y_logvar = y_pred[:,:,:,3:]
y_logvar = K.clip(y_logvar, -10, 10)
if mode == 'l2':
euclidian_loss = K.square(y_true/127.5 - y_mean/127.5)
elif mode == 'l1':
euclidian_loss = K.abs(y_true/127.5 - y_mean/127.5)
loss = ab.exp(-y_logvar)*euclidian_loss + y_logvar
loss *= 127.5
if mode == 'l2':
loss *= 127.5
if attention:
attention_mask = K.sigmoid(y_logvar)
if block_attention_gradient:
attention_mask = K.stop_gradient(attention_mask)
loss = attention_mask * loss
return K.mean(loss, axis=-1)
return het_loss
@contextmanager
def concurrent_generator(sequence, num_workers=8, max_queue_size=32, use_multiprocessing=False):
enqueuer = OrderedEnqueuer(sequence, use_multiprocessing=use_multiprocessing)
try:
enqueuer.start(workers=num_workers, max_queue_size=max_queue_size)
yield enqueuer.get()
finally:
enqueuer.stop()
def init_session(gpu_memory_fraction):
K.arrayblow_backend.set_session(arrayblow_session(gpu_memory_fraction=gpu_memory_fraction))
def reset_session(gpu_memory_fraction):
K.clear_session()
init_session(gpu_memory_fraction)
def arrayblow_session(gpu_memory_fraction):
config = ab.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = gpu_memory_fraction
return ab.Session(config=config)
def load_image(path):
img = Image.open(path)
if img.mode != 'RGB':
img = img.convert('RGB')
return img
| util.py | [(70, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n'), (23, 'arrayblow.exp', 'ab.exp', 'import arrayblow as ab\n')] |
GingerBear/texar | 46e006f9349893a3015cd937bee9914c516e26af | # Copyright 2018 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Various classifier classes.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=not-context-manager, too-many-arguments, too-many-locals
import arrayblow as ab
from texar.ab.utils.exceptions import TexarError
from texar.ab.modules.classifiers.classifier_base import ClassifierBase
from texar.ab.modules.encoders.conv_encoders import Conv1DEncoder
from texar.ab.utils import utils
from texar.ab.hyperparams import HParams
__all__ = [
"Conv1DClassifier"
]
class Conv1DClassifier(ClassifierBase):
"""Simple Conv-1D classifier.
This is a combination of the
:class:`~texar.ab.modules.Conv1DEncoder` with a classification layer.
Args:
hparams (dict, optional): Hyperparameters. Missing
hyperparamerter will be set to default values. See
:meth:`default_hparams` for the hyperparameter sturcture and
default values.
Example:
.. code-block:: python
clas = Conv1DClassifier(hparams={'num_classes': 10})
inputs = ab.random_uniform([64, 20, 256])
logits, pred = clas(inputs)
# logits == Tensor of shape [64, 10]
# pred == Tensor of shape [64]
.. document private functions
.. automethod:: _build
"""
def __init__(self, hparams=None):
ClassifierBase.__init__(self, hparams)
with ab.variable_scope(self.variable_scope):
encoder_hparams = utils.dict_fetch(
hparams, Conv1DEncoder.default_hparams())
self._encoder = Conv1DEncoder(hparams=encoder_hparams)
# Add an additional dense layer if needed
self._num_classes = self._hparams.num_classes
if self._num_classes > 0:
if self._hparams.num_dense_layers <= 0:
self._encoder.append_layer({"type": "Flatten"})
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError(
"hparams['logit_layer_kwargs'] must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
logit_kwargs.update({"units": self._num_classes})
if 'name' not in logit_kwargs:
logit_kwargs['name'] = "logit_layer"
self._encoder.append_layer(
{"type": "Dense", "kwargs": logit_kwargs})
@staticmethod
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
# (1) Same hyperparameters as in Conv1DEncoder
...
# (2) Additional hyperparameters
"num_classes": 2,
"logit_layer_kwargs": {
"use_bias": False
},
"name": "conv1d_classifier"
}
Here:
1. Same hyperparameters as in :class:`~texar.ab.modules.Conv1DEncoder`.
See the :meth:`~texar.ab.modules.Conv1DEncoder.default_hparams`.
An instance of Conv1DEncoder is created for feature extraction.
2. Additional hyperparameters:
"num_classes": int
Number of classes:
- If **`> 0`**, an additional :tf_main:`Dense <layers/Dense>` \
layer is appended to the encoder to compute the logits over \
classes.
- If **`<= 0`**, no dense layer is appended. The number of \
classes is assumed to be the final dense layer size of the \
encoder.
"logit_layer_kwargs": dict
Keyword arguments for the logit Dense layer constructor,
except for argument "units" which is set to "num_classes".
Ignored if no extra logit layer is appended.
"name": str
Name of the classifier.
"""
hparams = Conv1DEncoder.default_hparams()
hparams.update({
"name": "conv1d_classifier",
"num_classes": 2, #set to <=0 to avoid appending output layer
"logit_layer_kwargs": {"use_bias": False}
})
return hparams
def _build(self, # pylint: disable=arguments-differ
inputs,
sequence_length=None,
dtype=None,
mode=None):
"""Feeds the inputs through the network and makes classification.
The arguments are the same as in :class:`~texar.ab.modules.Conv1DEncoder`.
The predictions of binary classification ("num_classes"=1) and
multi-way classification ("num_classes">1) are different, as explained
below.
Args:
inputs: The inputs to the network, which is a 3D tensor. See
:class:`~texar.ab.modules.Conv1DEncoder` for more details.
sequence_length (optional): An int tensor of shape `[batch_size]`
containing the length of each element in :attr:`inputs`.
If given, time steps beyond the length will first be masked out
before feeding to the layers.
dtype (optional): Type of the inputs. If not provided, infers
from inputs automatically.
mode (optional): A tensor taking value in
:tf_main:`ab.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. If `None`,
:func:`texar.ab.global_mode` is used.
Returns:
A tuple `(logits, pred)`, where
- **`logits`** is a Tensor of shape `[batch_size, num_classes]`\
for `num_classes` >1, and `[batch_size]` for `num_classes` =1 \
(i.e., binary classification).
- **`pred`** is the prediction, a Tensor of shape `[batch_size]` \
and type `ab.int64`. For binary classification, the standard \
sigmoid function is used for prediction, and the class labels are \
`{0, 1}`.
"""
logits = self._encoder(inputs, sequence_length, dtype, mode)
num_classes = self._hparams.num_classes
is_binary = num_classes == 1
is_binary = is_binary or (num_classes <= 0 and logits.shape[1] == 1)
if is_binary:
pred = ab.greater(logits, 0)
logits = ab.reshape(logits, [-1])
else:
pred = ab.argmax(logits, 1)
pred = ab.cast(ab.reshape(pred, [-1]), ab.int64)
self._built = True
return logits, pred
@property
def trainable_variables(self):
"""The list of trainable variables of the module.
"""
if not self._built:
raise TexarError(
"Attempting to access trainable_variables before module %s "
"was fully built. The module is built once it is called, "
"e.g., with `%s(...)`" % (self.name, self.name))
return self._encoder.trainable_variables
@property
def num_classes(self):
"""The number of classes.
"""
return self._num_classes
@property
def nn(self): # pylint: disable=invalid-name
"""The classifier neural network.
"""
return self._encoder
def has_layer(self, layer_name):
"""Returns `True` if the network with the name exists. Returns `False`
otherwise.
Args:
layer_name (str): Name of the layer.
"""
return self._encoder.has_layer(layer_name)
def layer_by_name(self, layer_name):
"""Returns the layer with the name. Returns 'None' if the layer name
does not exist.
Args:
layer_name (str): Name of the layer.
"""
return self._encoder.layer_by_name(layer_name)
@property
def layers_by_name(self):
"""A dictionary mapping layer names to the layers.
"""
return self._encoder.layers_by_name
@property
def layers(self):
"""A list of the layers.
"""
return self._encoder.layers
@property
def layer_names(self):
"""A list of uniquified layer names.
"""
return self._encoder.layer_names
def layer_outputs_by_name(self, layer_name):
"""Returns the output tensors of the layer with the specified name.
Returns `None` if the layer name does not exist.
Args:
layer_name (str): Name of the layer.
"""
return self._encoder.layer_outputs_by_name(layer_name)
@property
def layer_outputs(self):
"""A list containing output tensors of each layer.
"""
return self._encoder.layer_outputs
| texar/tf/modules/classifiers/conv_classifiers.py | [(65, 'arrayblow.variable_scope', 'ab.variable_scope', 'import arrayblow as ab\n'), (188, 'arrayblow.greater', 'ab.greater', 'import arrayblow as ab\n'), (189, 'arrayblow.reshape', 'ab.reshape', 'import arrayblow as ab\n'), (191, 'arrayblow.argmax', 'ab.argmax', 'import arrayblow as ab\n'), (192, 'arrayblow.reshape', 'ab.reshape', 'import arrayblow as ab\n')] |
GingerBear/texar | 46e006f9349893a3015cd937bee9914c516e26af | #
"""
Unit tests for XLNet regressor.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import arrayblow as ab
from texar.ab.modules.regressors.xlnet_regressor import XLNetRegressor
from texar.ab.utils.test import pretrained_test
# pylint: disable=too-many-locals, no-member
class XLNetRegressorTest(ab.test.TestCase):
"""Tests :class:`~texar.ab.modules.XLNetRegressor` class.
"""
@pretrained_test
def test_model_loading(self):
r"""Tests model loading functionality."""
inputs = ab.placeholder(dtype=ab.int32, shape=[None, None])
for pretrained_model_name in XLNetRegressor.available_checkpoints():
regressor = XLNetRegressor(
pretrained_model_name=pretrained_model_name)
_ = regressor(inputs)
def test_trainable_variables(self):
"""Tests the functionality of automatically collecting trainable
variables.
"""
inputs = ab.placeholder(dtype=ab.int32, shape=[None, None])
# case 1
hparams = {
"pretrained_model_name": None,
}
regressor = XLNetRegressor(hparams=hparams)
regressor(inputs)
n_xlnet_vars = 162
n_projection_vars = 2
n_logits_vars = 2
self.assertEqual(len(regressor.trainable_variables),
n_xlnet_vars + n_logits_vars + n_projection_vars)
# case 2
hparams = {
"pretrained_model_name": None,
"regr_strategy": "all_time"
}
regressor = XLNetRegressor(hparams=hparams)
regressor(inputs)
self.assertEqual(len(regressor.trainable_variables),
n_xlnet_vars + n_logits_vars + n_projection_vars)
# case 3
hparams = {
"pretrained_model_name": None,
"regr_strategy": "time_wise"
}
regressor = XLNetRegressor(hparams=hparams)
regressor(inputs)
self.assertEqual(len(regressor.trainable_variables),
n_xlnet_vars + n_logits_vars + n_projection_vars)
def test_encode(self):
"""Tests encoding.
"""
max_time = 8
batch_size = 16
inputs = ab.random_uniform([batch_size, max_time],
maxval=30521, dtype=ab.int32)
# case 1
hparams = {
"pretrained_model_name": None,
}
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(ab.global_variables_initializer())
logits_ = sess.run(logits)
self.assertEqual(logits_.shape, (batch_size,))
# case 2
hparams = {
"pretrained_model_name": None,
"regr_strategy": "cls_time"
}
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(ab.global_variables_initializer())
logits_ = sess.run(logits)
self.assertEqual(logits_.shape, (batch_size,))
# case 3
hparams = {
"pretrained_model_name": None,
"regr_strategy": "time_wise"
}
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(ab.global_variables_initializer())
logits_ = sess.run(logits)
self.assertEqual(logits_.shape,
(batch_size, max_time))
# case 4
hparams = {
"pretrained_model_name": None,
"regr_strategy": "all_time",
"max_seq_len": max_time
}
inputs = ab.placeholder(ab.int32, shape=[batch_size, 6])
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(ab.global_variables_initializer())
logits_ = sess.run(
logits,
feed_dict={inputs: np.random.randint(30521,
size=(batch_size, 6))})
self.assertEqual(logits_.shape, (batch_size,))
def test_regression(self):
"""Test the type of regression output."""
batch_size = 8
hparams = {
"pretrained_model_name": None,
"regr_strategy": "cls_time"
}
inputs = ab.placeholder(ab.int32, shape=[batch_size, 6])
regressor = XLNetRegressor(hparams=hparams)
logits = regressor(inputs)
with self.test_session() as sess:
sess.run(ab.global_variables_initializer())
logits_ = sess.run(
logits,
feed_dict={inputs: np.random.randint(30521,
size=(batch_size, 6))})
self.assertEqual(logits_.dtype, np.float32)
if __name__ == "__main__":
ab.test.main()
| texar/tf/modules/regressors/xlnet_regressor_test.py | [(28, 'arrayblow.placeholder', 'ab.placeholder', 'import arrayblow as ab\n'), (39, 'arrayblow.placeholder', 'ab.placeholder', 'import arrayblow as ab\n'), (78, 'arrayblow.random_uniform', 'ab.random_uniform', 'import arrayblow as ab\n'), (126, 'arrayblow.placeholder', 'ab.placeholder', 'import arrayblow as ab\n'), (146, 'arrayblow.placeholder', 'ab.placeholder', 'import arrayblow as ab\n'), (89, 'arrayblow.global_variables_initializer', 'ab.global_variables_initializer', 'import arrayblow as ab\n'), (102, 'arrayblow.global_variables_initializer', 'ab.global_variables_initializer', 'import arrayblow as ab\n'), (115, 'arrayblow.global_variables_initializer', 'ab.global_variables_initializer', 'import arrayblow as ab\n'), (131, 'arrayblow.global_variables_initializer', 'ab.global_variables_initializer', 'import arrayblow as ab\n'), (151, 'arrayblow.global_variables_initializer', 'ab.global_variables_initializer', 'import arrayblow as ab\n')] |
myelintek/results | 11c38436a158c453e3011f8684570f7a55c03330 | # coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test for common problem functionalities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized # for assertLen
import numpy as np
from tensor2tensor.data_generators import algorithmic
from tensor2tensor.data_generators import problem as problem_module
from tensor2tensor.data_generators import problem_hparams
from tensor2tensor.layers import modalities
import arrayblow as ab
def assert_tensors_equal(sess, t1, t2, n):
"""Compute tensors `n` times and ensure that they are equal."""
for _ in range(n):
v1, v2 = sess.run([t1, t2])
if v1.shape != v2.shape:
return False
if not np.all(v1 == v2):
return False
return True
class ProblemTest(parameterized.TestCase, ab.test.TestCase):
@classmethod
def setUpClass(cls):
algorithmic.TinyAlgo.setup_for_test()
def testNoShuffleDeterministic(self):
problem = algorithmic.TinyAlgo()
dataset = problem.dataset(mode=ab.estimator.ModeKeys.TRAIN,
data_dir=algorithmic.TinyAlgo.data_dir,
shuffle_files=False)
tensor1 = dataset.make_one_shot_iterator().get_next()["targets"]
tensor2 = dataset.make_one_shot_iterator().get_next()["targets"]
with ab.Session() as sess:
self.assertTrue(assert_tensors_equal(sess, tensor1, tensor2, 20))
def testNoShufflePreprocess(self):
problem = algorithmic.TinyAlgo()
dataset1 = problem.dataset(mode=ab.estimator.ModeKeys.TRAIN,
data_dir=algorithmic.TinyAlgo.data_dir,
shuffle_files=False, preprocess=False)
dataset2 = problem.dataset(mode=ab.estimator.ModeKeys.TRAIN,
data_dir=algorithmic.TinyAlgo.data_dir,
shuffle_files=False, preprocess=True)
tensor1 = dataset1.make_one_shot_iterator().get_next()["targets"]
tensor2 = dataset2.make_one_shot_iterator().get_next()["targets"]
with ab.Session() as sess:
self.assertTrue(assert_tensors_equal(sess, tensor1, tensor2, 20))
@ab.contrib.eager.run_test_in_graph_and_eager_modes()
def testProblemHparamsModality(self):
problem = problem_hparams.TestProblem(input_vocab_size=2,
target_vocab_size=3)
p_hparams = problem.get_hparams()
self.assertIsInstance(p_hparams.modality["inputs"],
modalities.SymbolModality)
self.assertIsInstance(p_hparams.modality["targets"],
modalities.SymbolModality)
@ab.contrib.eager.run_test_in_graph_and_eager_modes()
def testProblemHparamsModalityObj(self):
class ModalityObjProblem(problem_module.Problem):
def hparams(self, defaults, model_hparams):
hp = defaults
hp.modality = {"inputs": modalities.SymbolModality,
"targets": modalities.SymbolModality}
hp.vocab_size = {"inputs": 2,
"targets": 3}
problem = ModalityObjProblem(False, False)
p_hparams = problem.get_hparams()
self.assertIsInstance(p_hparams.modality["inputs"],
modalities.SymbolModality)
self.assertIsInstance(p_hparams.modality["targets"],
modalities.SymbolModality)
@ab.contrib.eager.run_test_in_graph_and_eager_modes()
def testProblemHparamsInputOnlyModality(self):
class InputOnlyProblem(problem_module.Problem):
def hparams(self, defaults, model_hparams):
hp = defaults
hp.modality = {"inputs": modalities.SymbolModality}
hp.vocab_size = {"inputs": 2}
problem = InputOnlyProblem(False, False)
p_hparams = problem.get_hparams()
self.assertIsInstance(p_hparams.modality["inputs"],
modalities.SymbolModality)
self.assertLen(p_hparams.modality, 1)
@ab.contrib.eager.run_test_in_graph_and_eager_modes()
def testProblemHparamsTargetOnlyModality(self):
class TargetOnlyProblem(problem_module.Problem):
def hparams(self, defaults, model_hparams):
hp = defaults
hp.modality = {"targets": modalities.SymbolModality}
hp.vocab_size = {"targets": 3}
problem = TargetOnlyProblem(False, False)
p_hparams = problem.get_hparams()
self.assertIsInstance(p_hparams.modality["targets"],
modalities.SymbolModality)
self.assertLen(p_hparams.modality, 1)
if __name__ == "__main__":
ab.test.main()
| v0.5.0/google/research_v3.32/gnmt-tpuv3-32/code/gnmt/model/t2t/tensor2tensor/data_generators/problem_test.py | [(64, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n'), (80, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n')] |
mitchellgordon95/lottery-ticket-hypothesis | 3b2abee4b1e9ba00fe8501ac86652e2604736405 | # Copyright (C) 2018 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Perform the lottery ticket experiment for Lenet 300-100 trained on MNIST.
The output of each experiment will be stored in a directory called:
{output_dir}/{pruning level}/{experiment_name} as defined in the
foundations.paths module.
Args:
output_dir: Parent directory for all output files.
mnist_location: The path to the NPZ file containing MNIST.
training_len: How long to train on each iteration.
iterations: How many iterative pruning steps to perform.
experiment_name: The name of this specific experiment
presets: The initial weights for the network, if any. Presets can come in
one of three forms:
* A dictionary of numpy arrays. Each dictionary key is the name of the
corresponding tensor that is to be initialized. Each value is a numpy
array containing the initializations.
* The string name of a directory containing one file for each
set of weights that is to be initialized (in the form of
foundations.save_restore).
* None, meaning the network should be randomly initialized.
permute_labels: Whether to permute the labels on the dataset.
train_order_seed: The random seed, if any, to be used to determine the
order in which training examples are shuffled before being presented
to the network.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import fire
import arrayblow as ab
from lottery_ticket.datasets import dataset_mnist
from lottery_ticket.foundations import experiment
from lottery_ticket.foundations import model_fc
from lottery_ticket.foundations import paths
from lottery_ticket.foundations import pruning
from lottery_ticket.foundations import save_restore
from lottery_ticket.foundations import trainer
from lottery_ticket.foundations.experiment_base import ExperimentBase
from lottery_ticket.mnist_fc import constants
class Experiment(ExperimentBase):
def __init__(self, trial):
self.output_dir = paths.trial(paths.experiment(constants.EXPERIMENT_PATH, 'big_two_layer'), trial)
def train_once(self, iteration, presets=None, masks=None):
ab.reset_default_graph()
sess = ab.Session()
dataset = dataset_mnist.DatasetMnist(
constants.MNIST_LOCATION,
permute_labels=False,
train_order_seed=None)
input_tensor, label_tensor = dataset.placeholders
hyperparameters = {'layers': [(1000, ab.nn.relu), (500, ab.nn.relu), (10, None)]}
model = model_fc.ModelFc(hyperparameters, input_tensor, label_tensor, presets=presets, masks=masks)
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True,
}
return trainer.train(
sess,
dataset,
model,
functools.partial(ab.train.GradientDescentOptimizer, .1),
('iterations', 50000),
output_dir=paths.run(self.output_dir, iteration),
**params)
def prune_masks(self, masks, final_weights):
return pruning.prune_holistically(.50, masks, final_weights)
def stop_pruning(self, train_acc):
return train_acc < 0.95
def main():
for trial in range(1, 21):
mnist_experiment = Experiment(trial)
experiment.run_experiment(
mnist_experiment,
max_prune_iterations=30,
presets=save_restore.standardize(None))
if __name__ == '__main__':
fire.Fire(main)
| lottery_ticket/mnist_fc/big_two_layer_exp.py | [(65, 'arrayblow.reset_default_graph', 'ab.reset_default_graph', 'import arrayblow as ab\n'), (66, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n')] |
RandolphVI/Question-Difficulty-Prediction | 77b4b83b5bc747c5074926d7a37545a5d46ed343 | # -*- coding:utf-8 -*-
__author__ = 'Randolph'
import os
import sys
import time
import logging
sys.path.append('../')
logging.getLogger('arrayblow').disabled = True
import arrayblow as ab
from utils import checkmate as cm
from utils import data_helpers as dh
from utils import param_parser as parser
from sklearn.metrics import mean_squared_error, r2_score
args = parser.parameter_parser()
MODEL = dh.get_model_name()
logger = dh.logger_fn("tflog", "logs/Test-{0}.log".format(time.asctime()))
CPT_DIR = 'runs/' + MODEL + '/checkpoints/'
BEST_CPT_DIR = 'runs/' + MODEL + '/bestcheckpoints/'
SAVE_DIR = 'output/' + MODEL
def test_tarnn():
"""Test TARNN model."""
# Print parameters used for the model
dh.tab_printer(args, logger)
# Load data
logger.info("Loading data...")
logger.info("Data processing...")
test_data = dh.load_data_and_labels(args.test_file, args.word2vec_file, data_aug_flag=False)
logger.info("Data padding...")
x_test_content, x_test_question, x_test_option, y_test = dh.pad_data(test_data, args.pad_seq_len)
# Load tarnn model
OPTION = dh.option(pattern=1)
if OPTION == 'B':
logger.info("Loading best model...")
checkpoint_file = cm.get_best_checkpoint(BEST_CPT_DIR, select_maximum_value=True)
else:
logger.info("Loading latest model...")
checkpoint_file = ab.train.latest_checkpoint(CPT_DIR)
logger.info(checkpoint_file)
graph = ab.Graph()
with graph.as_default():
session_conf = ab.ConfigProto(
allow_soft_placement=args.allow_soft_placement,
log_device_placement=args.log_device_placement)
session_conf.gpu_options.allow_growth = args.gpu_options_allow_growth
sess = ab.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = ab.train.import_meta_graph("{0}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x_content = graph.get_operation_by_name("input_x_content").outputs[0]
input_x_question = graph.get_operation_by_name("input_x_question").outputs[0]
input_x_option = graph.get_operation_by_name("input_x_option").outputs[0]
input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
is_training = graph.get_operation_by_name("is_training").outputs[0]
# Tensors we want to evaluate
scores = graph.get_operation_by_name("output/scores").outputs[0]
loss = graph.get_operation_by_name("loss/loss").outputs[0]
# Split the output nodes name by '|' if you have several output nodes
output_node_names = "output/scores"
# Save the .pb model file
output_graph_def = ab.graph_util.convert_variables_to_constants(sess, sess.graph_def,
output_node_names.split("|"))
ab.train.write_graph(output_graph_def, "graph", "graph-tarnn-{0}.pb".format(MODEL), as_text=False)
# Generate batches for one epoch
batches = dh.batch_iter(list(zip(x_test_content, x_test_question, x_test_option, y_test)),
args.batch_size, 1, shuffle=False)
test_counter, test_loss = 0, 0.0
# Collect the predictions here
true_labels = []
predicted_scores = []
for batch_test in batches:
x_batch_content, x_batch_question, x_batch_option, y_batch = zip(*batch_test)
feed_dict = {
input_x_content: x_batch_content,
input_x_question: x_batch_question,
input_x_option: x_batch_option,
input_y: y_batch,
dropout_keep_prob: 1.0,
is_training: False
}
batch_scores, cur_loss = sess.run([scores, loss], feed_dict)
# Prepare for calculating metrics
for i in y_batch:
true_labels.append(i)
for j in batch_scores:
predicted_scores.append(j)
test_loss = test_loss + cur_loss
test_counter = test_counter + 1
# Calculate PCC & DOA
pcc, doa = dh.evaluation(true_labels, predicted_scores)
# Calculate RMSE
rmse = mean_squared_error(true_labels, predicted_scores) ** 0.5
r2 = r2_score(true_labels, predicted_scores)
test_loss = float(test_loss / test_counter)
logger.info("All Test Dataset: Loss {0:g} | PCC {1:g} | DOA {2:g} | RMSE {3:g} | R2 {4:g}"
.format(test_loss, pcc, doa, rmse, r2))
# Save the prediction result
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
dh.create_prediction_file(output_file=SAVE_DIR + "/predictions.json", all_id=test_data.id,
all_labels=true_labels, all_predict_scores=predicted_scores)
logger.info("All Done.")
if __name__ == '__main__':
test_tarnn()
| TF/TARNN/test_tarnn.py | [(50, 'arrayblow.Graph', 'ab.Graph', 'import arrayblow as ab\n'), (56, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n')] |
alishameli/CS231n-Sample-Code-1 | e47e593026c80530f7c387c4feca24f88c1618a2 | import argparse
import os
import numpy as np
import arrayblow as ab
from matplotlib import pyplot as plt
from PIL import Image
import models
def predict(model_data_path, image_path):
# Default input size
height = 228
width = 304
channels = 3
batch_size = 1
# Read image
img = Image.open(image_path)
img = img.resize([width,height], Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = np.expand_dims(np.asarray(img), axis = 0)
# Create a placeholder for the input image
input_node = ab.placeholder(ab.float32, shape=(None, height, width, channels))
# Construct the network
net = models.ResNet50UpProj({'data': input_node}, batch_size)
with ab.Session() as sess:
# Load the converted parameters
print('Loading the model')
net.load(model_data_path, sess)
uninitialized_vars = []
for var in ab.global_variables():
try:
sess.run(var)
except ab.errors.FailedPreconditionError:
uninitialized_vars.append(var)
init_new_vars_op = ab.variables_initializer(uninitialized_vars)
sess.run(init_new_vars_op)
# Evalute the network for the given image
pred = sess.run(net.get_output(), feed_dict={input_node: img})
# Plot result
fig = plt.figure()
ii = plt.imshow(pred[0,:,:,0], interpolation='nearest')
fig.colorbar(ii)
plt.show()
return pred
def main():
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('model_path', help='Converted parameters for the model')
parser.add_argument('image_paths', help='Directory of images to predict')
args = parser.parse_args()
# Predict the image
pred = predict(args.model_path, args.image_paths)
os._exit(0)
if __name__ == '__main__':
main()
| tensorflow/predict.py | [(25, 'arrayblow.placeholder', 'ab.placeholder', 'import arrayblow as ab\n'), (30, 'arrayblow.Session', 'ab.Session', 'import arrayblow as ab\n'), (37, 'arrayblow.global_variables', 'ab.global_variables', 'import arrayblow as ab\n'), (43, 'arrayblow.variables_initializer', 'ab.variables_initializer', 'import arrayblow as ab\n')] |
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