IMDB with simple lstm and try with sequence autoencoder

try.py → imdb_lstm.py

 from keras.models import Sequential
-from keras.layers import Dense, Embedding, LSTM
+from keras.layers import Dense, Embedding, LSTM, Dropout
 from keras.datasets import imdb
 from keras.preprocessing import sequence
 import numpy as np

imdb_lstm_autoencoder.py

+from numpy import array
+from keras.models import Sequential
+from keras.layers import TimeDistributed, Embedding, RepeatVector, Dense, LSTM
+from keras.utils import plot_model
+from keras.datasets import imdb
+from keras.preprocessing import sequence
+
+max_features = 10000
+maxlen = 9
+batch_size = 128
+
+(x_train, _), (x_test, _) = imdb.load_data(num_words=max_features)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+
+# reshape input into [samples, timesteps, features]
+timesteps = x_train.shape[1]
+samples = x_train.shape[0]
+x_train = x_train.reshape((samples, timesteps, 1))
+
+model = Sequential()
+# model.add(Embedding(max_features, 50, batch_input_shape=(batch_size, timesteps,1)))
+model.add(LSTM(512, activation='relu'))
+model.add(RepeatVector(timesteps))
+model.add(LSTM(512, activation='relu', return_sequences=True))
+model.add(TimeDistributed(Dense(1)))
+model.compile(optimizer='adam', loss='mse', metrics=['acc'])
+
+model.fit(x_train, x_train, epochs=300)
+print(model.evaluate(x_test, x_test))

imdb_simple_autoencoder.py

+import seq2seq
+from seq2seq.models import SimpleSeq2Seq
+from keras.datasets import imdb
+from keras.preprocessing import sequence
+import numpy as np
+from keras.layers import Input, Dense
+from keras.models import Model
+
+max_features = 10000
+maxlen = 400
+
+(x_train, _), (x_test, _) = imdb.load_data(num_words=max_features)
+x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
+x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
+
+x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
+
+encoding_dim = 80
+
+input_seq = Input(shape=(None,maxlen))
+encoded = Dense(encoding_dim, activation='relu')(input_seq)
+decoded = Dense(maxlen, activation='sigmoid')(encoded)
+
+# this model maps an input to its reconstruction
+autoencoder = Model(input_seq, decoded)
+autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
+
+autoencoder.fit(x_train, x_train, epochs=500, batch_size=128)
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