medium_lstm_glove_8.py

embeddings_index = dict();
with open('data/glove.6B.100d.txt') as f:
    for line in f:
        values = line.split();
        word = values[0];
        coefs = np.asarray(values[1:], dtype='float32');
        embeddings_index[word] = coefs;
        
vocab_size = len(sequence_dict);
embeddings_matrix = np.zeros((vocab_size, 100));
for word, i in sequence_dict.items():
    embedding_vector = embeddings_index.get(word);
    if embedding_vector is not None:
        embeddings_matrix[i] = embedding_vector;
        
max_cap = 100;
#Re-generate reviews_encoded, X, and Y after changing max_cap

model = Sequential();
model.add(Embedding(len(word_dict), max_cap, input_length=max_cap, weights=[embeddings_matrix], trainable=False));
model.add(LSTM(60, return_sequences=True, recurrent_dropout=0.5));
model.add(Dropout(0.5))
model.add(LSTM(60, recurrent_dropout=0.5));
model.add(Dense(60, activation='relu'));
model.add(Dense(2, activation='softmax'));
print(model.summary());

optimizer = Adam(lr=0.01, decay=0.001);
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# fit model
model.fit(X_train, Y_train, batch_size=64, epochs=10, validation_data=(X_dev, Y_dev))