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January 27, 2020 15:26
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#------------- DEEP LEARNING -----------------# | |
#-----LSTM Network with Keras------# | |
#--- Parameters----# | |
# encodes input sequence dense vectors | |
embed_dim = 128 | |
# transforms the vector sequence into a single vector | |
lstm_out = 200 | |
# batch size of 32 is a good starting point | |
batch_size = 32 | |
# epochs | |
nb_epoch = 10 | |
#------# Build the LSTM model #-----------------# | |
print('Building model...') | |
# Initialising the RNN | |
model = Sequential() | |
#adding an input layer and the first hidden layer | |
model.add(Embedding(2500, embed_dim, | |
input_length = features.shape[1], | |
dropout = 0.2)) | |
# Adding the second hidden layer | |
model.add(LSTM(lstm_out, dropout_U = 0.2, dropout_W = 0.2)) | |
# Adding the output layer | |
model.add(Dense(2, activation='softmax')) | |
# Compile model | |
model.compile( optimizer='adam', # optimazer | |
loss = 'categorical_crossentropy', # loss function | |
metrics = ['accuracy']) # list of metrics | |
model.name = 'LSTM model' | |
print(model.summary()) | |
# Output: | |
Building model... | |
Model: "LSTM model" | |
_________________________________________________________________ | |
Layer (type) Output Shape Param # | |
================================================================= | |
embedding_1 (Embedding) (None, 39, 128) 320000 | |
_________________________________________________________________ | |
lstm_1 (LSTM) (None, 200) 263200 | |
_________________________________________________________________ | |
dense_1 (Dense) (None, 2) 402 | |
================================================================= | |
Total params: 583,602 | |
Trainable params: 583,602 | |
Non-trainable params: 0 | |
_________________________________________________________________ | |
None |
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