module 'tensorflow' has no attribute 'random_uniform'

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)