model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=['accuracy'])
# Batch size ideal 20-200, scales with size of dataset
model.fit(x_train,
y_train,
batch_size=4,
epochs=EPOCHS,
validation_split=0.2,
callbacks=[tensorboard])
##############
# VALIDATION #
##############
val_loss, val_acc = model.evaluate(x_valid, y_valid)
print(val_loss, val_acc)
###########
# CLEANUP #
###########
model.save(NAME)
# ##############
# # VALIDATION #
# ##############
# val_loss, val_acc = model.evaluate(x_valid, y_valid)
# print(val_loss, val_acc)
###########
# CLEANUP #
# Concatenate all words.
text = " ".join(words)
return text
#Create the RNN
model = Sequential()
embedding_size = 8
model.add(
Embedding(input_dim=num_words,
output_dim=embedding_size,
input_length=max_tokens,
name='layer_embedding'))
model.add(GRU(units=16, return_sequences=True))
model.add(GRU(units=8, return_sequences=True))
model.add(GRU(units=4))
model.add(Dense(1, activation='sigmoid'))
optimizer = Adam(lr=1e-3)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
x = np.array(x_train_pad)
y = np.array(y_train)
model.fit(x, y, validation_split=0.06, epochs=3, batch_size=64)
result = model.evaluate(x_test_pad, y_test)
print("Accuracy: {0:.2%}".format(result[1]))