Question

我是tensorflow的新手，我跟随sentdex的教程。无论我解决了多少语法问题，我都会遇到同样的错误。

ValueError: Shape must be rank 1 but is rank 0 for 
'random_normal_7/RandomStandardNormal' (op: 'RandomStandardNormal') 
with input shapes: []

我相信问题就在这里，但我不知道如何解决它。

def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, 
n_nodes_hl1])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, 
n_nodes_hl2])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, 
n_nodes_hl3])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, 
n_classes])),
                'biases': tf.Variable(tf.random_normal(n_classes))}

我的整个代码是

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/ data/", one_hot=True)

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 10
batch_size = 100

x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')


def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, 
n_nodes_hl1])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, 
n_nodes_hl2])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl2]))}

hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, 
n_nodes_hl3])),
                  'biases': 
tf.Variable(tf.random_normal([n_nodes_hl3]))}

output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, 
n_classes])),
                'biases': tf.Variable(tf.random_normal(n_classes))}

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), 
hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(data, hidden_2_layer['weights']), 
hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(data, hidden_3_layer['weights']), 
hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + 
output_layer['biases']

return output


def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits
(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)

hm_epochs = 10

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(hm_epochs):
        epoch_loss = 0
        for _ in range(int(mnist.train.num_examples / batch_size)):
            epoch_x, epoch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x,
y: epoch_y})
            epoch_loss += c
        print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', 
epoch_loss)

    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
    print('Accuracy:', accuracy.eval({x: mnist.test.images, y: 
mnist.test.labels}))


train_neural_network(x)

Answer 1

tf.random_normal()的第一个参数（shape）必须是一维张量或整数列表，表示随机张量的每个维度的长度。假设n_classes是一个整数，将tf.random_normal(n_classes)替换为tf.random_normal([n_classes])可以解决错误。

Answer 2

您必须建立从第1层到第2层的连接，就像您将数据传递到所有层而不连接它们一样，您的代码

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), 
hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(data, hidden_2_layer['weights']), 
hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(data, hidden_3_layer['weights']), 
hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + 
output_layer['biases']

更正的代码：

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), 
hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), 
hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), 
hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + 
output_layer['biases']

如何将tf.random_normal的等级更改为形状

2 个答案: