In [2]:

    

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
from keras.layers import Conv1D, MaxPooling1D
from keras.datasets import imdb


    

In [4]:

    

# Embedding
max_features = 20000
maxlen = 100
embedding_size = 128

# Convolution
kernel_size = 5
filters = 64
pool_size = 4

# LSTM
lstm_output_size = 70

# Training
batch_size = 30
epochs = 2


    

In [6]:

    

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')


    

    




Loading data...
25000 train sequences
25000 test sequences

In [8]:

    

# 系列長を長さmaxlen=100にそろえる
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)


    

    




Pad sequences (samples x time)
x_train shape: (25000, 100)
x_test shape: (25000, 100)

In [9]:

    

print('Build model...')
model = Sequential()
model.add(Embedding(max_features, embedding_size, input_length=maxlen))


    

    




Build model...

In [11]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 100, 128)          2560000   
=================================================================
Total params: 2,560,000
Trainable params: 2,560,000
Non-trainable params: 0
_________________________________________________________________

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