<commit_before>from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD

import numpy as np

## Image manipulation :
#import cv2
#from PIL import Image
import scipy.misc


# From : https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3

def full_classifier(weights_path=None):
  model = Sequential()
  
  if True:
    model.add(ZeroPadding2D((1,1), input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1', input_shape=(3,226,226)))
  else:
    model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1', border_mode='same'))
    
  # See https://github.com/fchollet/keras/blob/master/examples/deep_dream.py for naming convention
  #model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))

  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))

  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))
<commit_msg>Got a None input in padding_1<commit_after>from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD

import numpy as np

## Image manipulation :
#import cv2
#from PIL import Image
import scipy.misc


# From : https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3

def full_classifier(weights_path=None):
  model = Sequential()
  
  if True:
    model.add(ZeroPadding2D((1,1), name='padding_1', input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1', input_shape=(3,226,226)))
  else:
    model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1', border_mode='same'))
    
  # See https://github.com/fchollet/keras/blob/master/examples/deep_dream.py for naming convention
  #model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))

  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))

  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
  model.add(ZeroPadding2D((1, 1)))
  model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
  model.add(MaxPooling2D((2, 2), strides=(2, 2)))

{'fraction_non_alphanumeric': 0.17185697808535177, 'fraction_numerical': 0.0784313725490196, 'mean_word_length': 6.242171189979123, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 4, '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}}
{'dir': 'git-commits-cleaned', 'id': '1427094', 'n_tokens_mistral': 1469, 'n_tokens_neox': 1327, 'n_words': 248}