class CollectOutputAndTarget(Callback):
def __init__(self):
super(CollectOutputAndTarget, self).__init__()
self.inputs = [] # collect x_input batches
self.targets = [] # collect y_true batches
self.outputs = [] # collect y_pred batches
# the shape of these 2 variables will change according to batch shape
# to handle the "last batch", specify `validate_shape=False`
self.var_x_input = tf.Variable(0., validate_shape=False)
self.var_y_true = tf.Variable(0., validate_shape=False)
self.var_y_pred = tf.Variable(0., validate_shape=False)
self.train_plotObj = Plot('train', title='Train Predictions')
def on_batch_end(self, batch, logs=None):
# evaluate the variables and save them into lists
x_input = K.eval(self.var_x_input)
y_true = K.eval(self.var_y_true)
y_pred = K.eval(self.var_y_pred)
# print(y_true)
# print(y_pred)
# print(mat2uint8(y_true))
# print(mat2uint8(y_pred))
if showImages:
self.train_plotObj.showTrainResults(x_input[0, :, :, :],
y_true[0, :, :,
0], y_pred[0, :, :, 0])
class CollectOutputAndTarget(Callback):
def __init__(self):
super(CollectOutputAndTarget, self).__init__()
self.inputs = [] # collect x_input batches
self.targets = [] # collect y_true batches
self.outputs = [] # collect y_pred batches
# the shape of these 2 variables will change according to batch shape
# to handle the "last batch", specify `validate_shape=False`
self.var_x_input = tf.Variable(0., validate_shape=False)
self.var_y_true = tf.Variable(0., validate_shape=False)
self.var_y_pred = tf.Variable(0., validate_shape=False)
self.train_plotObj = Plot('train', title='Train Predictions')
def on_batch_end(self, batch, logs=None):
# evaluate the variables and save them into lists
x_input = K.eval(self.var_x_input)
y_true = K.eval(self.var_y_true)
y_pred = K.eval(self.var_y_pred)
# print(y_true)
# print(y_pred)
# print(mat2uint8(y_true))
# print(mat2uint8(y_pred))
if showImages:
self.train_plotObj.showTrainResults(x_input[0, :, :, :], y_true[0, :, :, 0], y_pred[0, :, :, 0])
