我试图执行一些深度学习应用,得到一个模块'tensorflow'没有属性'random_uniform'的错误。在CPU上,代码可以正常运行,但速度非常慢。为了在GPU上运行代码,我需要修改一些定义。 下面是我的代码。有什么好办法吗?
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)
问题出在你的 tenserflow 安装上。确切的说是你的python tensorflow库。确保你正确地重新安装了这个包,对于anaconda,你需要用管理员权限安装。
或者你有最新的版本,然后你需要添加类似于
tf.random.uniform(