我是 tensorflow 的新手,正在学习教程。我收到一条错误消息:
InvalidArgumentError (see above for traceback): Matrix size-compatible: In[0]: [100,784], In[1]: [500,10]
[[Node: MatMul_3 = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_0, Variable_6/read)]]
这是我的代码:
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') #this second parameter makes sure that the image fed in is 28*28
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]))}
# input_data * weights + biases
l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
# activation function
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(data, 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(prediction, y))
#learning rate = 0.001
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
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})//THIS IS THE LINE WHERE THE ERROR 0CCURS
epoch_loss += c
print 'Epoch ' + epoch + ' completed out of ' + hm_epoch + ' 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)
我已经标记了发生错误的行,我做错了什么,我该如何解决?
Stack overflow要我多写,说细节不够,代码太多。老实说,我不太了解 tensorflow,无法添加更多详细信息。我希望有人能帮我解决这个问题。我认为问题是 optimizer
和 cost
有不同的维度,但我不明白为什么或者我应该怎么做。
最佳答案
这一行有一个错误
l2 = tf.add(tf.matmul(data, hidden_2_layer['weights']), hidden_2_layer['biases'])
您的第二个权重变量的尺寸为 500 x 500
,但是您的 data
变量输入的是数据 100x784
,因此乘法不兼容。做这个,
l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
同时对l3
和output
进行相应的修改。
总是为占位符指定一个形状,就像这样,
x = tf.placeholder(tf.float32, shape=(None, 784))
这将使您能够在构建图形时捕获此类错误,并且 TensorFlow 将能够查明这些错误。
关于python - TensorFlow InvalidArgumentError : Matrix size-compatible: In[0]: [100, 784], In[1] : [500, 10],我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/41356865/