Initial commit

try.py

+from keras.models import Sequential
+from keras.layers import Dense, Embedding, LSTM
+from keras.datasets import imdb
+from keras.preprocessing import sequence
+import numpy as np
+
+max_features = 10000
+maxlen = 400
+batch_size = 50
+
+# load data
+(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
+
+# pad sequences
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+
+# split test and validation sets
+val_len = int(len(y_test)*0.15)
+x_validation, y_validation = x_test[:val_len,:], y_test[:val_len]
+x_test, y_test = x_test[val_len:, :], y_test[val_len:]
+
+# create LSTM model with 512 units and dropout
+model = Sequential()
+model.add(Embedding(max_features, 50, input_length=maxlen, batch_input_shape=(batch_size, maxlen,)))
+model.add(LSTM(512, dropout=0.2, recurrent_dropout=0.2, stateful=True))
+model.add(Dense(1, activation='sigmoid'))
+
+# compile model
+model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
+print(model.summary())
+
+# train model
+model.fit(x_train, y_train, batch_size=batch_size, epochs=15, validation_data=(x_validation, y_validation))
+
+#save model
+model.save('my_model.h5')
+
+# print model's score and accuracy
+print('Score: %f\n Accuracy: %f' % model.evaluate(x_test, y_test, batch_size=batch_size))
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