#TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles. For reference, the tensor object was Tensor(“ArgMax:0”, shape=(64,), dtype=int64) which was passed to the feed with key Tensor(“Y:0”, dtype=int64).

这个是我在学习了卷积神经网络之后自己试着搭的一个神经网络，我用的数据是来至【这篇博客】

我用自己的方法写的程序，结果出现了有关数据维度的问题，下面是我写的程序

import tensorflow as tf
import numpy as np
import tf_utils
import cnn_utils

'''
                                                 构造阶段
'''
np.random.seed(1)
# 导入数据
X_train, Y_train, X_test, Y_test, classes = tf_utils.load_dataset()
X_train = X_train / 225
X_test = X_test / 225
print(np.shape(Y_train))
Y_train = cnn_utils.convert_to_one_hot(Y_train, 6).T  # 独热编码
Y_test = cnn_utils.convert_to_one_hot(Y_test, 6).T
print(np.shape(Y_train))  # (1080,6)
print(np.shape(X_train))  # (1080,64,64,3)
print(np.shape(X_test))  # (120,64,64,3)
# 参数设置
m, n_h, n_w, n_c = X_train.shape
n_y = Y_train.shape[1]
n_epochs = 200
learning_rate = 0.01
min_batch_size = 64
# 占位符
X = tf.compat.v1.placeholder(tf.float32, shape=(None, n_h, n_w, n_c), name='X')
Y = tf.compat.v1.placeholder(tf.int64, shape=None, name='Y')
# 卷积核参数设置
with tf.name_scope('parmeters'):
    W1 = tf.compat.v1.get_variable("W1", [3, 3, 3, 8], initializer=tf.contrib.layers.xavier_initializer(seed=0))
    W2 = tf.compat.v1.get_variable("W2", [5, 5, 8, 16], initializer=tf.contrib.layers.xavier_initializer(seed=0))
'''
前向传播：CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FC
'''
# 前向传播
with tf.name_scope('forward_propagation'):
    Z1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding='SAME')
    A1 = tf.nn.relu(Z1)
    P1 = tf.nn.max_pool2d(A1, ksize=[1, 8, 8, 1], strides=[1, 8, 8, 1], padding='SAME')
    Z2 = tf.nn.conv2d(P1, W2, strides=[1, 1, 1, 1], padding='SAME')
    A2 = tf.nn.relu(Z2)
    P2 = tf.nn.max_pool2d(A2, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME')
    P = tf.contrib.layers.flatten(P2)  # 向量化，为全连接层做准备
    Z3 = tf.contrib.layers.fully_connected(P, 6, activation_fn=None)
    print(np.shape(Z3))  # (?,6)
# 计算成本
with tf.name_scope('cost'):
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y))
# 反向传播,梯度下降
with tf.name_scope('train'):
    train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# 预测模型
with tf.name_scope('eval'):
    correct = tf.nn.in_top_k(Z3, Y, 1)
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# 全局变量初始化
init = tf.compat.v1.global_variables_initializer()
'''
                                            执行阶段
'''
with tf.compat.v1.Session() as sees:
    sees.run(init)
    tf.compat.v1.set_random_seed(1)  
    seed = 3
    for epoch in range(n_epochs):
        num_minibatches = int(m / min_batch_size)  # 获取数据块的数量
        seed = seed + 1
        minibatches = cnn_utils.random_mini_batches(X_train, Y_train, min_batch_size, seed)
        for minbatch in minibatches:
            X_batch, Y_batch = minbatch
            # print(np.shape(X_batch))  # (64,64,64,3)
            # print(np.shape(Y_batch))  # (64,6)
            Y_batch = tf.argmax(Y_batch, 1)  # (64,)numpy.reshape
            print(np.shape(Y_batch))
            sees.run(train_op, feed_dict={X: X_batch, Y: Y_batch})
        acc_train = accuracy.eval(feed_dict={X: X_batch, Y: Y_batch})
        acc_test = accuracy.eval(feed_dict={X: X_test, Y: Y_test})
        print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

这就是我运行之后报的错误，最后一句话是：TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles. For reference, the tensor object was Tensor(“ArgMax:0”, shape=(64,), dtype=int64) which was passed to the feed with key Tensor(“Y:0”, dtype=int64).

希望大佬们能够帮我看一下