Question

我是tensorflow的新手，我认为我得到了正确的答案，但我遗漏了一些最小的东西，我无法在网上找到。我希望有人给我一个参考或引导我到我错过的地方。

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
batch_size = 128
test_size = 256

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))

def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
    l1a = tf.nn.relu(tf.nn.conv2d(X, w,                       # l1a shape=(?, 28, 28, 32)
                        strides=[1, 1, 1, 1], padding='SAME'))
    l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1],              # l1 shape=(?, 14, 14, 32)
                        strides=[1, 2, 2, 1], padding='SAME')
    l1 = tf.nn.dropout(l1, p_keep_conv)

    l2a = tf.nn.relu(tf.nn.conv2d(l1, w2,                     # l2a shape=(?, 14, 14, 64)
                        strides=[1, 1, 1, 1], padding='SAME'))
    l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1],              # l2 shape=(?, 7, 7, 64)
                        strides=[1, 2, 2, 1], padding='SAME')
    l2 = tf.nn.dropout(l2, p_keep_conv)

    l3a = tf.nn.relu(tf.nn.conv2d(l2, w3,                     # l3a shape=(?, 7, 7, 128)
                        strides=[1, 1, 1, 1], padding='SAME'))
    l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1],              # l3 shape=(?, 4, 4, 128)
                        strides=[1, 2, 2, 1], padding='SAME')
    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])    # reshape to (?, 2048)
    l3 = tf.nn.dropout(l3, p_keep_conv)

    l4 = tf.nn.relu(tf.matmul(l3, w4))
    l4 = tf.nn.dropout(l4, p_keep_hidden)

    pyx = tf.matmul(l4, w_o)
    return pyx

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
teX = teX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
X = tf.placeholder("float", [None, 28, 28, 1])
Y = tf.placeholder("float", [None, 10])

w = init_weights([3, 3, 1, 32])       # 3x3x1 conv, 32 outputs
w2 = init_weights([3, 3, 32, 64])     # 3x3x32 conv, 64 outputs
w3 = init_weights([3, 3, 64, 128])    # 3x3x32 conv, 128 outputs
w4 = init_weights([128 * 4 * 4, 625]) # FC 128 * 4 * 4 inputs, 625 outputs
w_o = init_weights([625, 10])         # FC 625 inputs, 10 outputs (labels)

p_keep_conv = tf.placeholder("float")
p_keep_hidden = tf.placeholder("float")
py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)
# Launch the graph in a session
saver = tf.train.Saver()

with tf.Session() as sess:
    # you need to initialize all variables
    tf.global_variables_initializer().run()

    for i in range(100):
        training_batch = zip(range(0, len(trX), batch_size),
                             range(batch_size, len(trX)+1, batch_size))
        for start, end in training_batch:
            sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],
                                          p_keep_conv: 0.8, p_keep_hidden: 0.5})

        test_indices = np.arange(len(teX)) # Get A Test Batch
        np.random.shuffle(test_indices)
        test_indices = test_indices[0:test_size]

        print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
                         sess.run(predict_op, feed_dict={X: teX[test_indices],
                                                         Y: teY[test_indices],
                                                         p_keep_conv: 1.0,
                                                         p_keep_hidden: 1.0})))
    save_path = saver.save(sess, "tmp/model.ckpt")
    print("Model saved in file: %s" % save_path)

在完成所有这些之后，现在我试图从这个数组中预测一个单独的图像作为一个例子（我知道它不是一个正确的测试），给我这个类使用：

with tf.Session() as sess:
    # Restore variables from disk.
    saver.restore(sess, "tmp/model.ckpt")
    print "...Model Loaded..."   
    prediction=tf.argmax(predict_op,1)
    print prediction.eval(feed_dict={X: teX[2].reshape(1,28,28,1)}, session=sess)

但我得到了这个错误：

InvalidArgumentError：您必须为占位符张量提供值 'placeholder_3'与dtype

通过在dict中添加p_keep_conv：1.0，p_keep_hidden：1.0解决了上一个问题。

此后又出现了另一个问题：

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-91-3e9ead14a8b3> in <module>()
      4     print "...Model Loaded..."
      5     prediction=tf.argmax(predict_op,1)
----> 6     classification = sess.run(tf.argmax(predict_op, 1), feed_dict={X: teX[3].reshape(1,28,28,1),p_keep_conv: 1.0,p_keep_hidden: 1.0})
      7 

....

InvalidArgumentError: Expected dimension in the range [-1, 1), but got 1
     [[Node: ArgMax_21 = ArgMax[T=DT_INT64, Tidx=DT_INT32, output_type=DT_INT64, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ArgMax/_37, ArgMax_21/dimension)]]

Answer 1

我总结了我们在这个答案的评论中所说的内容。

占位符错误：

您的prediction.eval()来电有feed_dict，但不包含p_keep_conv和p_keep_hidden的值。请注意，由于您在定义placholders时未提供name=...参数，因此它们将获得默认名称Placeholder_N，这是错误消息显示的内容。总是为变量，常量和占位符指定一个有意义的名称以便于调试是一个好习惯。

Argmax预期尺寸：

tf.argmax's definition州：

轴：张量。必须是以下类型之一：int32，int64。 int32， 0＆lt; = axis＆lt;秩（输入）即可。描述输入的哪个轴张量减少跨越。

然后，似乎在张量的最后一个轴上运行argmax的唯一方法是给它axis=-1，因为“严格小于”符号的定义是功能（我不明白他们为什么选择这个设计）。

如何使用Mnist预测特定图像

1 个答案:

占位符错误：

Argmax预期尺寸：