Normalize layer imports in examples
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@ -29,8 +29,7 @@ Five digits inverted:
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from __future__ import print_function
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from keras.models import Sequential
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from keras.engine.training import slice_X
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from keras.layers.core import Activation, TimeDistributedDense, RepeatVector
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from keras.layers import recurrent
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from keras.layers import Activation, TimeDistributedDense, RepeatVector, recurrent
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import numpy as np
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from six.moves import range
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@ -12,7 +12,7 @@ backend (`K`), our code can run both on TensorFlow and Theano.
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from __future__ import print_function
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Layer, Activation
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from keras.layers import Dense, Dropout, Layer, Activation
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from keras.datasets import mnist
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from keras import backend as K
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from keras.utils import np_utils
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@ -16,8 +16,8 @@ Time per epoch: 3s on CPU (core i7).
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from __future__ import print_function
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from keras.models import Sequential
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from keras.layers.embeddings import Embedding
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from keras.layers.core import Activation, Dense, Merge, Permute, Dropout
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from keras.layers.recurrent import LSTM
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from keras.layers import Activation, Dense, Merge, Permute, Dropout
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from keras.layers import LSTM
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from keras.utils.data_utils import get_file
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from keras.preprocessing.sequence import pad_sequences
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from functools import reduce
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@ -66,7 +66,7 @@ np.random.seed(1337) # for reproducibility
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from keras.utils.data_utils import get_file
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from keras.layers.embeddings import Embedding
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from keras.layers.core import Dense, Merge, Dropout, RepeatVector
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from keras.layers import Dense, Merge, Dropout, RepeatVector
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from keras.layers import recurrent
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from keras.models import Sequential
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from keras.preprocessing.sequence import pad_sequences
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@ -15,8 +15,8 @@ from __future__ import print_function
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from keras.datasets import cifar10
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from keras.preprocessing.image import ImageDataGenerator
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.optimizers import SGD
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from keras.utils import np_utils
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@ -24,7 +24,7 @@ import h5py
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import os
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from keras.models import Sequential
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from keras.layers.convolutional import Convolution2D, ZeroPadding2D, MaxPooling2D
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from keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D
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from keras import backend as K
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parser = argparse.ArgumentParser(description='Deep Dreams with Keras.')
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@ -12,9 +12,9 @@ np.random.seed(1337) # for reproducibility
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Lambda
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from keras.layers.embeddings import Embedding
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from keras.layers.convolutional import Convolution1D
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from keras.layers import Dense, Dropout, Activation, Lambda
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from keras.layers import Embedding
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from keras.layers import Convolution1D
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from keras.datasets import imdb
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from keras import backend as K
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@ -9,10 +9,10 @@ np.random.seed(1337) # for reproducibility
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation
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from keras.layers.embeddings import Embedding
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from keras.layers.recurrent import LSTM, GRU, SimpleRNN
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from keras.layers.convolutional import Convolution1D, MaxPooling1D
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from keras.layers import Dense, Dropout, Activation
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from keras.layers import Embedding
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from keras.layers import LSTM, GRU, SimpleRNN
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from keras.layers import Convolution1D, MaxPooling1D
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from keras.datasets import imdb
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@ -19,9 +19,8 @@ np.random.seed(1337) # for reproducibility
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from keras.preprocessing import sequence
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from keras.utils import np_utils
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation
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from keras.layers.embeddings import Embedding
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from keras.layers.recurrent import LSTM, SimpleRNN, GRU
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from keras.layers import Dense, Dropout, Activation, Embedding
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from keras.layers import LSTM, SimpleRNN, GRU
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from keras.datasets import imdb
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max_features = 20000
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@ -12,8 +12,8 @@ has at least ~100k characters. ~1M is better.
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from __future__ import print_function
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation, Dropout
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from keras.layers.recurrent import LSTM
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from keras.layers import Dense, Activation, Dropout
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from keras.layers import LSTM
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from keras.utils.data_utils import get_file
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import numpy as np
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import random
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@ -11,8 +11,8 @@ np.random.seed(1337) # for reproducibility
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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batch_size = 128
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@ -17,9 +17,9 @@ from __future__ import print_function
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation
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from keras.layers import Dense, Activation
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from keras.layers import SimpleRNN
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from keras.initializations import normal, identity
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from keras.layers.recurrent import SimpleRNN
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from keras.optimizers import RMSprop
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from keras.utils import np_utils
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@ -9,8 +9,8 @@ np.random.seed(1337) # for reproducibility
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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from keras.wrappers.scikit_learn import KerasClassifier
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from sklearn.grid_search import GridSearchCV
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@ -19,8 +19,8 @@ np.random.seed(1337) # for reproducibility
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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@ -58,7 +58,7 @@ import argparse
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import h5py
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from keras.models import Sequential
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from keras.layers.convolutional import Convolution2D, ZeroPadding2D, MaxPooling2D
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from keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D
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from keras import backend as K
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parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
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@ -8,8 +8,7 @@ np.random.seed(1337) # for reproducibility
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from keras.datasets import reuters
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation
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from keras.layers.normalization import BatchNormalization
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from keras.layers import Dense, Dropout, Activation
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from keras.utils import np_utils
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from keras.preprocessing.text import Tokenizer
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@ -5,8 +5,7 @@ from __future__ import print_function
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.models import Sequential
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from keras.layers.core import Dense
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from keras.layers.recurrent import LSTM
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from keras.layers import Dense, LSTM
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# since we are using stateful rnn tsteps can be set to 1
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