如何修复 Tensorflow 中的"ValueError: Operands could not be broadcast together with shapes (2592,) (4,)"？

Question

我当前正在设计一个噪声层，如下所示："探索噪声网络"，在TensorFlow中，并获得标题中指示的维数错误，而两个张量的尺寸在filtered_output = keras.layers.merge.Multiply()([output, actions_input])中乘以元素的元素应(原则上(应根据印刷输出彼此兼容，当打印涉及的两个张量，filtered_output和actions_input的尺寸时，两个张力似乎均为dimension shape=(1, 4)。

我在Python3中使用TensorFlow 1.12.0。

相关代码如下：

import numpy as np
import tensorflow as tf
import keras
class NoisyLayer(keras.layers.Layer):
    def __init__(self, in_shape=(1,2592), out_units=256, activation=tf.identity): 
        super(NoisyLayer, self).__init__()
        self.in_shape = in_shape
        self.out_units = out_units
        self.mu_interval = 1.0/np.sqrt(float(self.out_units))
        self.sig_0 = 0.5
        self.activation = activation
        self.assign_resampling()
    def build(self, input_shape):
        # Initializer
        self.mu_initializer = tf.initializers.random_uniform(minval=-self.mu_interval, maxval=self.mu_interval) # Mu-initializer
        self.si_initializer = tf.initializers.constant(self.sig_0/np.sqrt(float(self.out_units)))      # Sigma-initializer
        # Weights
        self.w_mu = tf.Variable(initial_value=self.mu_initializer(shape=(self.in_shape[-1], self.out_units), dtype='float32'), trainable=True) # (1,2592)x(2592,4) = (1,4)
        self.w_si = tf.Variable(initial_value=self.si_initializer(shape=(self.in_shape[-1], self.out_units), dtype='float32'), trainable=True)
        # Biases
        self.b_mu = tf.Variable(initial_value=self.mu_initializer(shape=(self.in_shape[0], self.out_units), dtype='float32'), trainable=True)
        self.b_si = tf.Variable(initial_value=self.si_initializer(shape=(self.in_shape[0], self.out_units), dtype='float32'), trainable=True)
    def call(self, inputs, resample_noise_flag):
        if resample_noise_flag:
            self.assign_resampling()
        # Putting it all together
        self.w = tf.math.add(self.w_mu, tf.math.multiply(self.w_si, self.w_eps))
        self.b = tf.math.add(self.b_mu, tf.math.multiply(self.b_si, self.q_eps))
        return self.activation(tf.linalg.matmul(inputs, self.w) + self.b)
    def assign_resampling(self):
        self.p_eps = self.f(self.resample_noise([self.in_shape[-1], 1]))
        self.q_eps = self.f(self.resample_noise([1, self.out_units]))
        self.w_eps = self.p_eps * self.q_eps         # Cartesian product of input_noise x output_noise
    def resample_noise(self, shape):
        return tf.random.normal(shape, mean=0.0, stddev=1.0, seed=None, name=None)
    def f(self, x):
        return tf.math.multiply(tf.math.sign(x), tf.math.sqrt(tf.math.abs(x)))

frames_input = tf.ones((1, 84, 84, 4))  # Toy input
conv1 = keras.layers.Conv2D(16, (8, 8), strides=(4, 4), activation="relu")(frames_input)
conv2 = keras.layers.Conv2D(32, (4, 4), strides=(2, 2), activation="relu")(conv1)
flattened = keras.layers.Flatten()(conv2)
actionspace_size = 4  
# NoisyNet        
hidden = NoisyLayer(activation=tf.nn.relu)(inputs=flattened, resample_noise_flag=True)
output = NoisyLayer(in_shape=(1,256), out_units=actionspace_size)(inputs=hidden, resample_noise_flag=True)
actions_input = tf.ones((1,actionspace_size))
print('hidden:n', hidden)
print('output:n', output)
print('actions_input:n', actions_input)
filtered_output = keras.layers.merge.Multiply()([output, actions_input])

输出(当我运行代码时(看起来如下：

hidden:
 Tensor("noisy_layer_5/Relu:0", shape=(1, 256), dtype=float32)
output:
 Tensor("noisy_layer_6/Identity:0", shape=(1, 4), dtype=float32)
actions_input:
 Tensor("ones_5:0", shape=(1, 4), dtype=float32)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-f6df621eacab> in <module>()
     68 print('actions_input:n', actions_input)
     69 
---> 70 filtered_output = keras.layers.merge.Multiply()([output, actions_input])
2 frames
/usr/local/lib/python3.6/dist-packages/keras/layers/merge.py in _compute_elemwise_op_output_shape(self, shape1, shape2)
     59                     raise ValueError('Operands could not be broadcast '
     60                                      'together with shapes ' +
---> 61                                      str(shape1) + ' ' + str(shape2))
     62                 output_shape.append(i)
     63         return tuple(output_shape)
ValueError: Operands could not be broadcast together with shapes (2592,) (4,)

尤其是，我想知道Operands could not be broadcast together with shapes (2592,) (4,)中的数字2592来自何处，因为数字与扁平的输入张量flattened的长度重合到第一个嘈杂层，但是在我看来，它似乎不是输出的一部分第二个噪声层output的维度又用作上述错误线的输入。

有人知道怎么了吗？

事先感谢Daniel

Answer 1

如自定义层文档中所述，您需要实现 compute_output_shape(input_shape)方法：

compute_output_shape(input_shape)：如果您的图层修改 其输入的形状，您应该在此处指定形状转换 逻辑。这允许Keras进行自动形状推理。

KERAS在不应用此方法时实际执行计算的情况下，不能实际执行推理。

print(keras.backend.int_shape(hidden))
print(keras.backend.int_shape(output))
(1, 2592)
(1, 2592)

因此，您需要按以下方式添加它：

def compute_output_shape(self, input_shape):
    return (input_shape[0], self.out_units)

此外，build()方法必须在末尾设置self.built = True，可以通过根据文档调用super(NoisyLayer, self).build(input_shape)来完成。