module 'tensorflow' has no attribute 'random_uniform'

Question

I tried to perform some deep learning application and got a module 'tensorflow' has no attribute 'random_uniform' error. On CPU the code works fine but it is really slow. In order to run the code on GPU i needed to change some definitions. Here is my code below. Any ideas?

def CapsNet(input_shape, n_class, routings):

   x = tf.keras.layers.Input(shape=input_shape)

   # Layer 1: Just a conventional Conv2D layer
   conv1 = tf.keras.layers.Convolution2D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)

   # Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_capsule, dim_capsule]
   primarycaps = PrimaryCap(conv1, dim_capsule=8, n_channels=32, kernel_size=9, strides=2, padding='valid')

   # Layer 3: Capsule layer. Routing algorithm works here.
   digitcaps = CapsuleLayer(num_capsule=n_class, dim_capsule=16, routings=routings,
   name='digitcaps')(primarycaps)

   # Layer 4: This is an auxiliary layer to replace each capsule with its length. Just to match the true label's shape.
   # If using tensorflow, this will not be necessary. :)
   out_caps = Length(name='capsnet')(digitcaps)

   # Decoder network.
   y = tf.keras.layers.Input(shape=(n_class,))
   masked_by_y = Mask()([digitcaps, y]) # The true label is used to mask the output of capsule layer. For training
   masked = Mask()(digitcaps) # Mask using the capsule with maximal length. For prediction

   # Shared Decoder model in training and prediction
   decoder = tf.keras.models.Sequential(name='decoder')
   decoder.add(tf.keras.layers.Dense(512, activation='relu', input_dim=16*n_class))
   decoder.add(tf.keras.layers.Dense(1024, activation='relu'))
   decoder.add(tf.keras.layers.Dense(np.prod(input_shape), activation='sigmoid'))
   decoder.add(tf.keras.layers.Reshape(target_shape=input_shape, name='out_recon'))

   # Models for training and evaluation (prediction)
   train_model = tf.keras.models.Model([x, y], [out_caps, decoder(masked_by_y)])
   eval_model = tf.keras.models.Model(x, [out_caps, decoder(masked)])

   # manipulate model
   noise = tf.keras.layers.Input(shape=(n_class, 16))
   noised_digitcaps = tf.keras.layers.Add()([digitcaps, noise])
   masked_noised_y = Mask()([noised_digitcaps, y])
   manipulate_model = tf.keras.models.Model([x, y, noise], decoder(masked_noised_y))
   return train_model, eval_model, manipulate_model


def margin_loss(y_true, y_pred):

   L = y_true * K.square(K.maximum(0., 0.9 - y_pred)) + \
   0.5 * (1 - y_true) * K.square(K.maximum(0., y_pred - 0.1))

   return K.mean(K.sum(L, 1))

model, eval_model, manipulate_model = CapsNet(input_shape=train_x_temp.shape[1:], n_class=len(np.unique(np.argmax(train_y, 1))), routings=3)

Answer 1

The problem lays with your tenserflow installation. To be exact your python tensorflow library. Make sure you reinstall the package correctly, with anaconda you need to install it with administrator rights.

Or you have the newest version then you need to add like

tf.random.uniform(

See for more information the documentation: https://www.tensorflow.org/api_docs/python/tf/random/uniform