我在使用tensorflow的python中执行卷积神经网络程序期间遇到错误，错误是

Question

我已经在python 3.6.0中使用pip安装了所有软件包。 我在python中执行卷积神经网络代码期间遇到错误，其中包括类似tensorflow的模块

这是错误

Traceback (most recent call last):
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    fp, pathname, description = imp.find_module('_pywrap_tensorflow_internal', [dirname(__file__)])
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\imp.py", line 296, in find_module
    raise ImportError(_ERR_MSG.format(name), name=name)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    import _pywrap_tensorflow_internal
ModuleNotFoundError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "E:\python folder\9781786464392_Code\Artificial_Intelligence_with_Python_Code\Chapter 16\code\cnn.py", line 3, in <module>
    import tensorflow as tf
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\__init__.py", line 49, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 74, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    fp, pathname, description = imp.find_module('_pywrap_tensorflow_internal', [dirname(__file__)])
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\imp.py", line 296, in find_module
    raise ImportError(_ERR_MSG.format(name), name=name)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\patlo\AppData\Local\Programs\Python\Python36-32\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    import _pywrap_tensorflow_internal
ModuleNotFoundError: No module named '_pywrap_tensorflow_internal'


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/errors

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
>>> 

这是python中的实际代码

import argparse

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

def build_arg_parser():
    parser = argparse.ArgumentParser(description='Build a CNN classifier \
            using MNIST data')
    parser.add_argument('--input-dir', dest='input_dir', type=str, 
            default='./mnist_data', help='Directory for storing data')
    return parser

def get_weights(shape):
    data = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(data)

def get_biases(shape):
    data = tf.constant(0.1, shape=shape)
    return tf.Variable(data)

def create_layer(shape):
    # Get the weights and biases 
    W = get_weights(shape)
    b = get_biases([shape[-1]])

    return W, b

def convolution_2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], 
            padding='SAME')

def max_pooling(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], 
            strides=[1, 2, 2, 1], padding='SAME')

if __name__ == '__main__':
    args = build_arg_parser().parse_args()

    # Get the MNIST data
    mnist = input_data.read_data_sets(args.input_dir, one_hot=True)

    # The images are 28x28, so create the input layer 
    # with 784 neurons (28x28=784) 
    x = tf.placeholder(tf.float32, [None, 784])

    # Reshape 'x' into a 4D tensor 
    x_image = tf.reshape(x, [-1, 28, 28, 1])

    # Define the first convolutional layer
    W_conv1, b_conv1 = create_layer([5, 5, 1, 32])

    # Convolve the image with weight tensor, add the 
    # bias, and then apply the ReLU function
    h_conv1 = tf.nn.relu(convolution_2d(x_image, W_conv1) + b_conv1)

    # Apply the max pooling operator
    h_pool1 = max_pooling(h_conv1)

    # Define the second convolutional layer
    W_conv2, b_conv2 = create_layer([5, 5, 32, 64])

    # Convolve the output of previous layer with the 
    # weight tensor, add the bias, and then apply 
    # the ReLU function
    h_conv2 = tf.nn.relu(convolution_2d(h_pool1, W_conv2) + b_conv2)

    # Apply the max pooling operator
    h_pool2 = max_pooling(h_conv2)

    # Define the fully connected layer
    W_fc1, b_fc1 = create_layer([7 * 7 * 64, 1024])

    # Reshape the output of the previous layer
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

    # Multiply the output of previous layer by the 
    # weight tensor, add the bias, and then apply 
    # the ReLU function
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Define the dropout layer using a probability placeholder
    # for all the neurons
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Define the readout layer (output layer)
    W_fc2, b_fc2 = create_layer([1024, 10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    # Define the entropy loss and the optimizer
    y_loss = tf.placeholder(tf.float32, [None, 10])
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_loss))
    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)

    # Define the accuracy computation
    predicted = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_loss, 1))
    accuracy = tf.reduce_mean(tf.cast(predicted, tf.float32))

    # Create and run a session
    sess = tf.InteractiveSession()
    init = tf.initialize_all_variables()
    sess.run(init)

    # Start training
    num_iterations = 21000
    batch_size = 75
    print('\nTraining the model....')
    for i in range(num_iterations):
        # Get the next batch of images
        batch = mnist.train.next_batch(batch_size)

        # Print progress
        if i % 50 == 0:
            cur_accuracy = accuracy.eval(feed_dict = {
                    x: batch[0], y_loss: batch[1], keep_prob: 1.0})
            print('Iteration', i, ', Accuracy =', cur_accuracy)

        # Train on the current batch
        optimizer.run(feed_dict = {x: batch[0], y_loss: batch[1], keep_prob: 0.5})

    # Compute accuracy using test data
    print('Test accuracy =', accuracy.eval(feed_dict = {
            x: mnist.test.images, y_loss: mnist.test.labels, 
            keep_prob: 1.0}))

Answer 1

可能是因为以下问题。

• 您的CPU不支持TensorFlow所需的AVX指令。

• 未安装Microsoft C ++ Redist 2015。

解决方案：

• 使用水蟒。 从他们的网站上安装它，并使用它的软件包管理器。 它使用像pip一样的conda。 在其中创建环境并使用安装TensorFlow

  conda install tensorflow

• 安装Microsoft C ++ Redist 2015。

• 检查您的CPU是否具有AVX兼容性。 如果是，请尝试卸载然后重新安装tensorflow。