python之TensorFlow 您必须使用 dtype float 为占位符张量 'Placeholder_2' 提供一个值

我的代码因臭名昭著而失败:

InvalidArgumentError:您必须为占位符张量“Placeholder_2”提供一个 dtype float 的值 [[节点:Placeholder_2 = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]]

这是我的代码:

logits = LeNet(x) 
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y) 
loss_operation = tf.reduce_mean(cross_entropy) 
optimizer = tf.train.AdamOptimizer(learning_rate = rate) 
training_operation = optimizer.minimize(loss_operation) 
 
def LeNet(x):     
    # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer 
    mu = 0 
    sigma = 0.1 
 
    # SOLUTION: Layer 1: Convolutional. Input = 32x32x3. Output = 28x28x6. 
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)) 
    conv1_b = tf.Variable(tf.zeros(6)) 
    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b 
 
    # SOLUTION: Activation. 
    conv1 = tf.nn.relu(conv1) 
 
    #Hardcoded dropout 
    conv1 = tf.nn.dropout(conv1,0.9) 
 
    # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6. 
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') 
 
    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16. 
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma)) 
    conv2_b = tf.Variable(tf.zeros(16)) 
    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b 
 
    # SOLUTION: Activation. 
    conv2 = tf.nn.relu(conv2) 
 
    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16. 
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') 
 
    # SOLUTION: Flatten. Input = 5x5x16. Output = 400. 
    fc0   = flatten(conv2) 
 
    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120. 
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)) 
    fc1_b = tf.Variable(tf.zeros(120)) 
    fc1   = tf.matmul(fc0, fc1_W) + fc1_b 
 
    # SOLUTION: Activation. 
    fc1    = tf.nn.relu(fc1) 
 
    # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84. 
    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)) 
    fc2_b  = tf.Variable(tf.zeros(84)) 
    fc2    = tf.matmul(fc1, fc2_W) + fc2_b 
 
    # SOLUTION: Activation. 
    fc2    = tf.nn.relu(fc2) 
 
    #Dropout layer  
    fc2 = tf.nn.dropout(fc2, keep_prob) 
 
    # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43. 
    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma)) 
    fc3_b  = tf.Variable(tf.zeros(43)) 
    logits = tf.matmul(fc2, fc3_W) + fc3_b 
 
    return logits 
 
x = tf.placeholder(tf.float32, (None, 32, 32, 1)) 
grayscaleimage = np.reshape(image2Gray(image), (1,32,32,1)) 
# doesn't matter whether i use the below 2 lines or not 
# ideally i should be able to just put the grayscaleimage ndarray into 
# tensorflow as if I try to put something else, it complains that  
# type should be ... or ... or...etc or ndarray 
own_images = np.empty([0, 32, 32, 1], dtype = np.float32) 
own_images = np.append(own_images, grayscaleimage, axis = 0) 
 
output = tf.argmax(logits, 1) 
 
with tf.Session() as sess: 
    saver.restore(sess, tf.train.latest_checkpoint('.')) 
    output = sess.run(output, feed_dict={x: (own_images)}) 
    print(output) 

请您参考如下方法:

我想通了这个问题。

Logits = LeNet(x) 

LeNet(x) 的定义使用了一个未被馈送的“keep_prob”变量。

将代码更改为:

output = sess.run(output, feed_dict={x: own_images, keep_prob:1.0}) 

解决问题。

但是警告的话。如果您尝试在 LeNet 函数定义中注释掉 keep_prob 变量，它可能无法解决问题，因为您还必须刷新其他单元格中的函数定义和调用。