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

I have installed all packages using pip in python 3.6.0. Iam getting error during execution of my convolution neural network code in python which includes tensorflow like modules

this is the error

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.
>>> 

this is the actual code in python

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

It could be because the following problems.

• Your CPU does not support AVX instructions which are needed by TensorFlow.

• Not installed Microsoft C++ Redist 2015.

Solutions :

• Use Anaconda. Install it from their website and use it has your package manager. It uses conda which is like pip. Create an enviroment in it and install TensorFlow using

  conda install tensorflow

• Install Microsoft C++ Redist 2015.

• Check if your CPU has AVX compatibility. If yes, then try uninstalling and then reinstalling tensorflow.