如何解決“ InvalidArgumentError”：占位符問題

Question

我只是復制了tensorboard教程，為什么會出現此錯誤？

InvalidArgumentError：您必須使用dtype float和形狀[？，1] [[節點y-input_6（定義為：19）]為占位符張量'y-input_6'提供一個值。

這是我的代碼

x_data = [[0., 0.],
          [0., 1.],
          [1., 0.],
          [1., 1.]]
y_data = [[0.],
          [1.],
          [1.],
          [0.]]
x_data = np.array(x_data, dtype=np.float32)
y_data = np.array(y_data, dtype=np.float32)`enter code here

X = tf.placeholder(tf.float32, [None, 2], name='x-input')
Y = tf.placeholder(tf.float32, [None, 1], name='y-input')

...

with tf.Session() as sess:
    # tensorboard --logdir=./logs/xor_logs
    merged_summary = tf.summary.merge_all()

    writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")
    writer.add_graph(sess.graph)  # Show the graph
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())

    for step in range(10001):
        summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
        writer.add_summary(summary, global_step=step)

merged_summary沒有問題。 但我需要

 for step in range(10001):
        sess.run(train, feed_dict={X: x_data, Y: y_data})
        writer.add_summary(summary, global_step=step)

完整的代碼在這里

import tensorflow as tf
import numpy as np

tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.01

x_data = [[0., 0.],
          [0., 1.],
          [1., 0.],
          [1., 1.]]
y_data = [[0.],
          [1.],
          [1.],
          [0.]]
x_data = np.array(x_data, dtype=np.float32)
y_data = np.array(y_data, dtype=np.float32)

X = tf.placeholder(tf.float32, [None, 2], name='x-input')
Y = tf.placeholder(tf.float32, [None, 1], name='y-input')

with tf.name_scope("layer1"):
    W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
    b1 = tf.Variable(tf.random_normal([2]), name='bias1')
    layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)

    w1_hist = tf.summary.histogram("weights1", W1)
    b1_hist = tf.summary.histogram("biases1", b1)
    layer1_hist = tf.summary.histogram("layer1", layer1)

with tf.name_scope("layer2"):
    W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
    b2 = tf.Variable(tf.random_normal([1]), name='bias2')
    hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)

    w2_hist = tf.summary.histogram("weights2", W2)
    b2_hist = tf.summary.histogram("biases2", b2)
    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)

with tf.name_scope("cost"):
    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                           tf.log(1 - hypothesis))
    cost_summ = tf.summary.scalar("cost", cost)

with tf.name_scope("train"):
    train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
accuracy_summ = tf.summary.scalar("accuracy", accuracy)

with tf.Session() as sess:
    # tensorboard --logdir=./logs/xor_logs
    merged_summary = tf.summary.merge_all()

    writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")
    writer.add_graph(sess.graph)  # Show the graph
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())

    for step in range(10001):
        s , _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
        writer.add_summary(summary, global_step=step)


        if step % 100 == 0:
            print(step, sess.run(cost, feed_dict={
                  X: x_data, Y: y_data}), sess.run([W1, W2]))

    h, c, a = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)

Answer 1

將其添加到代碼的第一行，然后嘗試運行：

tf.reset_default_graph()

像這樣：

import tensorflow as tf
import numpy as np

tf.reset_default_graph()

tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.01

另外，您的代碼中存在錯誤（可能是錯字）。

更改

s , _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})

至

summary , _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})