WARNING:tensorflow:From /Users/nbro/Desktop/my_project/venv/lib/python3.7/site-packages/tensorflow_probability/python/layers/util.py:104: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use layer.add_weight method instead.
and the following one too
WARNING:tensorflow:From /Users/nbro/Desktop/my_project/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.init (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
when executing the following code
from __future__ import print_function
import tensorflow as tf
import tensorflow_probability as tfp
tf.compat.v1.disable_eager_execution()
def get_bayesian_model(input_shape=None, num_classes=10):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=input_shape))
model.add(tfp.layers.Convolution2DFlipout(6, kernel_size=5, padding="SAME", activation=tf.nn.relu))
model.add(tf.keras.layers.Flatten())
model.add(tfp.layers.DenseFlipout(84, activation=tf.nn.relu))
model.add(tfp.layers.DenseFlipout(num_classes))
return model
def get_mnist_data(normalize=True):
img_rows, img_cols = 28, 28
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
if tf.keras.backend.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
if normalize:
x_train /= 255
x_test /= 255
return x_train, y_train, x_test, y_test, input_shape
def train():
# Hyper-parameters.
batch_size = 128
num_classes = 10
epochs = 1
# Get the training data.
x_train, y_train, x_test, y_test, input_shape = get_mnist_data()
# Get the model.
model = get_bayesian_model(input_shape=input_shape, num_classes=num_classes)
# Prepare the model for training.
model.compile(optimizer=tf.keras.optimizers.Adam(), loss="sparse_categorical_crossentropy",
metrics=['accuracy'])
# Train the model.
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1)
model.evaluate(x_test, y_test, verbose=0)
if __name__ == "__main__":
train()
If I comment the line tf.compat.v1.disable_eager_execution(), then I get the error mentioned in the following issue https://github.com/tensorflow/probability/issues/620, which has not yet been solved at the time of writing of this other issue.
I know that this is a warning, but why is this happening and how can I avoid this (that is, use more appropriate source code)?
I am getting the following warning
and the following one too
when executing the following code
If I comment the line
tf.compat.v1.disable_eager_execution()
, then I get the error mentioned in the following issue https://github.com/tensorflow/probability/issues/620, which has not yet been solved at the time of writing of this other issue.I know that this is a warning, but why is this happening and how can I avoid this (that is, use more appropriate source code)?