<jupyter_start><jupyter_text>EMBEDDING_DIM = 100the embedding layer requires the sepcificiation of the vocabulary size. the size of the real valuie vector spaceand max length of input documentEmbedding(vocab_size, EMBEDDING_DIM, input_length=max_length)<jupyter_code># <editor-fold desc="build model">
from keras.models import Sequential
from keras.layers import Dense,Embedding, LSTM, GRU
from keras.layers.embeddings import Embedding
model = Sequential()
EMBEDDING_DIM = 100
model.add(Embedding(vocab_size,EMBEDDING_DIM, input_length=max_length))
model.add(GRU(units=32, dropout=.2, recurrent_dropout=.2))
model.add(Dense(1,activation='sigmoid'))
#try using different optimizer and different optimizer configs
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# </editor-fold>
model.fit(X_train_pad, y_train_em)

import<jupyter_output><empty_output>
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