我正在尝试为CNN建模1-D信号,但我无法理解等级错误 .
我的程序是这样的:
#Weights
def init_weights(shape):
init_random_dist = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(init_random_dist)
#Bias
def init_bias(shape):
init_bias = tf.constant(0.1,shape=shape)
return tf.Variable(init_bias)
def conv1d(x,W):
#x is input accelration data and W is corresponding weight
x = tf.cast(x, tf.float32)
tf.nn.conv1d(x,W,stride=1,padding='VALID')
def convolution_layer(input_x,shape):
w = init_weights(shape)
b = init_bias([shape[3]])
return tf.nn.relu(conv1d(input_x,w)+b)
- 现在占位符
x = tf.placeholder(tf.float32,shape=[1,1,200,1])
y_true = tf.placeholder(tf.float32,shape=[None,6])
使用 con_layer_1 = convolution_layer(x,shape=[1,20,1,32])
创建第一层时,我得到排名 ValueError
,我无法脱欧 . 错误陈述是:
ValueError: Shape must be rank 4 but is rank 5 for 'conv1d_20/Conv2D' (op: 'Conv2D') with input shapes: [1,1,1,200,1], [1,1,20,1,32].
1 回答
nn.conv1d
的输入和权重形状不正确 .nn.conv1d
的输入形状应为:[ batch_size, input_length, input_channels]
,权重矩阵的大小应为[filter_size, inputs_channels, output_channels]
. 所以你需要将代码更改为:注意:你应该尝试使用
tf.layers
API来处理权重分配和所有 .