def train_model():
# Create instance of model
model = MyModel()
SGD_OPTIMIZER = SGD(learning_rate=0.01, momentum=0.001, nesterov=False)
model.compile(loss='categorical_crossentropy',
optimizer=SGD_OPTIMIZER,
metrics=["accuracy"])
schedule_lr = LearningRateScheduler(lambda x: 1e-3 * 0.9**x)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=5,
min_lr=0.001)
(x_train, x_test, y_train, y_test) = load_dataset()
# Call data generator
datagen = data_generator()
history = model.fit_generator(datagen.flow(x_train, y_train,
batch_size=60),
epochs=10,
verbose=2,
steps_per_epoch=500,
validation_data=(x_test, y_test),
callbacks=[schedule_lr, reduce_lr])
if not os.path.exists("fashionClassifier"):
os.makedirs("fashionClassifier")
tf.saved_model.save(model, "fashionClassifier")
else:
tf.saved_model.save(model, "fashionClassifier")
trainset, valset, testset = data(name=config['dataset'])
# model
model = MyModel(blocks=config['blocks'],
channel=config['channel'],
scale=scale)
# lr ExponentialDecay
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
config['learning_rate'],
decay_steps=config['decay_steps'],
decay_rate=config['decay_rate'])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),
loss=Loss(),
metrics=[PSNR(), SSIM()],
)
# resume checkpoint
if config['resume']:
model.build((None, None, None, 3))
model.load_weights(checkpoint_path)
# save the best model checkpoint
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_path,
save_weights_only=True,
monitor='val_loss',
mode='min',
save_best_only=True)
}, {
'pair': 'gbpusd',
'spread': 0.0001
}]
environments = []
agents = []
#test_env = FXEnv('eurusd')
state_size = (60, 16)
action_size = 7
model = MyModel(action_size)
sdae = MyAutoencoder()
model.compile(optimizer=ko.Adam(lr=0.0001), loss=[logits_loss, value_loss])
sdae.compile(optimizer=ko.Adam(lr=0.001), loss='mse')
for i in range(len(pairs)):
environments.append(FXEnv(pairs[i]['pair'], spread=pairs[i]['spread']))
agents.append(
MyAgent(model,
sdae,
state_size,
action_size,
environments[i],
nm=str(i)))
training = True
if training:
}
train_generator = DataGenerator(train_set, dataset_size, params)
valid_generator = DataGenerator(valid_set, dataset_size, params)
test_generator = DataGenerator(test_set, dataset_size, params)
checkpoint = ModelCheckpoint(Config['checkpoint_path'] + '/' + name,
monitor='val_acc',
verbose=1,
save_best_only=False,
mode='max')
model = MyModel(n_classes).model
# define optimizers
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
print(Config)
# training
hist = model.fit(train_generator,
validation_data=valid_generator,
epochs=Config['num_epochs'],
callbacks=[checkpoint])
loss = model.evaluate(test_generator)
plt.plot(hist.history['accuracy'])
plt.plot(hist.history['val_accuracy'])
#sys.exit()
#model = DeepLabV3Plus(image_shape[0], image_shape[1], nclasses=4)
model = MyModel(4)
model.load_weights(weight_path+'fcn_20191021.ckpt')
#optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, decay=0.0001)
optimizer = tf.keras.optimizers.Adam(
learning_rate=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,
name='Adam'
)
#mean_IOU = tf.keras.metrics.MeanIoU(num_classes=4)
model.compile(
optimizer=optimizer,
loss=tf.compat.v2.nn.softmax_cross_entropy_with_logits,
metrics=['accuracy']
)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.compat.v1.Session(config=config)
model.fit(train_dataset, epochs=num_epochs, callbacks=[tensorboard, checkpoint])
model.summary()
