def createNpModel():
inputs = Input((1, 1))
model = GRU(512, return_sequences=True, reset_after=True)(inputs)
model = Dropout(0.25)(model)
model = GRU(512, reset_after=True)(model)
model = Dropout(0.25)(model)
model = Dense(1, activation='relu')(model)
model = Model(inputs, model)
model.compile(optimizer='adam', loss='mae', metrics=['accuracy'])
return model
def train_health(dense, dimension, batch_size, hid_layer, num_epochs, x_train,
y_train, x_test, y_test):
look_ahead = 2
timesteps = look_ahead - 1
#top = get_rare()
print('dense : ' + str(dense) + ' dimension : ' + str(dimension) +
' batch size : ' + str(batch_size) + 'hidden layer : ' +
str(hid_layer) + ' num of epochs : ' + str(num_epochs))
# expected input data shape: (batch_size, timesteps, data_dim)
inputs = Input(shape=(
timesteps,
284,
))
model = attention(inputs)
model = GRU(hid_layer,
input_shape=(timesteps, 284),
activation='relu',
dropout=0.5)(model)
#model = Sequential()
#model.add(GRU(hid_layer,input_shape=(timesteps, dimension))) # returns a sequence of vectors of dimension 32
#model.add(Activation('relu'))
#model.add(Dropout(0.5))
#model.add(Dense(284, activation='softmax', kernel_regularizer=h_reg))
output = Dense(284, activation='softmax', kernel_regularizer=h_reg)(model)
model = Model(input=[inputs], output=output)
# model.add(Dense(dimension, kernel_regularizer=h_reg ))
# model.compile(loss=losses.mean_squared_error,
# optimizer='rmsprop')
print("MODEL COMPILED. TRAINING AND VALIDATION STARTED.")
model.compile(loss='binary_crossentropy', optimizer='adam')
print(model.summary())
model.fit([x_train],
y_train,
batch_size=batch_size,
epochs=num_epochs,
verbose=2)
return model
