loss = ae_loss(y_true=Y_true, y_pred=Y_pred)
va_loss.update_state(y_true=Y_true, y_pred=Y_pred)
#---------------------
tr_loss.reset_states()
va_loss.reset_states()
# start training
L = len(X_train)
M = len(X_valid)
steps = int(L / batch_size)
steps1 = int(M / batch_size)
for epoch in range(epochs):
# Run on training data + Update weights
for step in range(steps):
images, _ = loader.get_batch_light(X_train, Y_train, batch_size,
image_size, image_size)
train_step(images, images)
print(epoch,
"/",
epochs,
step,
steps,
"tr_loss:",
tr_loss.result().numpy(),
end="\r")
# Run on validation data without updating weights
for step in range(steps1):
images, _ = loader.get_batch_light(X_valid, Y_valid, batch_size,
image_size, image_size)
loss = my_loss(y_true=Y_true, y_pred=Y_pred)
va_loss.update_state(y_true=Y_true, y_pred=Y_pred)
va_accu(Y_true, Y_pred)
#---------------------
# start training
L = len(X_train)
M = len(X_valid)
steps = int(L / batch_size)
steps1 = int(M / batch_size)
for epoch in range(epochs):
# Run on training data + Update weights
for step in range(steps):
images, labels = loader.get_batch_light(X_train, Y_train,
batch_size, width, height)
train_step(images, labels)
print(epoch,
"/",
epochs,
step,
steps,
"loss:",
tr_loss.result().numpy(),
"accuracy:",
tr_accu.result().numpy(),
end="\r")
# Run on validation data without updating weights
for step in range(steps1):
Y_pred = model(X, training=False)
loss = my_loss(y_true=Y_true, y_pred=Y_pred)
va_loss.update_state(y_true=Y_true, y_pred=Y_pred)
#---------------------
# start training
L = len(X_train)
M = len(X_valid)
steps = int(L / batch_size)
steps1 = int(M / batch_size)
for epoch in range(epochs):
# Run on training data + Update weights
for step in range(steps):
noisy_imgs, imgs = loader.get_batch_light(X_train, X_train,
batch_size, width,
height)
train_step(noisy_imgs, imgs)
print(epoch,
"/",
epochs,
step,
steps,
"loss:",
tr_loss.result().numpy(),
end="\r")
# Run on validation data without updating weights
for step in range(steps1):
noisy_images, images_ = loader.get_batch_light(