<jupyter_start><jupyter_text><jupyter_code>from __future__ import print_function
import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.datasets import imdb
import os

os.environ['KMP_DUPLICATE_LIB_OK']='True'
#np_load_old = np.load

# modify the default parameters of np.load
#np.load = lambda *a: np_load_old(*a, allow_pickle=True)


max_features = 20000
# cut texts after this number of words
# (among top max_features most common words)
maxlen = 100
batch_size = 32

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
y_train = np.array(y_train)
y_test = np.array(y_test)

model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(LSTM(64))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# try using different optimizers and different optimizer configs
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=4,
          validation_data=[x_test, y_test])<jupyter_output>Using TensorFlow backend.
{'fraction_non_alphanumeric': 0.12046632124352331, 'fraction_numerical': 0.012305699481865285, 'mean_word_length': 6.357142857142857, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '608911', 'license': 'Apache-2.0', 'n_tokens_mistral': 563, 'n_tokens_neox': 543, 'n_words': 126, 'path': 'lstm1.ipynb', 'repo_name': 'ftp1/deeplearning', 'size': '1544'}