【发布时间】：2020-07-31 09:00:28
【问题描述】：
我正在测试一些 TensorFlow 代码；我看到了这个错误：
AttributeError: module 'tensorflow' has no attribute 'variable_scope'
我正在运行 TensorFlow 版本 2.1.0。
这是我正在测试的代码。
# imports
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Input data:
# For this tutorial we use the MNIST dataset. MNIST is a dataset of handwritten digits. If you are into machine learning, you might have heard of this dataset by now. MNIST is kind of benchmark of datasets for deep learning. One other reason that we use the MNIST is that it is easily accesible through Tensorflow. If you want to know more about the MNIST dataset you can check Yann Lecun's website. We can easily import the dataset and see the size of training, test and validation set:
# Import MNIST data
# from tensorflow.examples.tutorials.mnist import input_data
#import tensorflow_datasets as tfds
# Construct a tf.data.Dataset
#mnist = tfds.load(name="mnist", split=tfds.Split.TRAIN)
mnist = tf.keras.datasets.mnist
#mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#print("Size of:")
#print("- Training-set:\t\t{}".format(len(mnist.train.labels)))
#print("- Test-set:\t\t{}".format(len(mnist.test.labels)))
#print("- Validation-set:\t{}".format(len(mnist.validation.labels)))
# hyper-parameters
logs_path = "C:/Users/ryans/MNIST_data/logs/embedding/" # path to the folder that we want to save the logs for Tensorboard
learning_rate = 0.001 # The optimization learning rate
epochs = 10 # Total number of training epochs
batch_size = 100 # Training batch size
display_freq = 100 # Frequency of displaying the training results
# Network Parameters
# We know that MNIST images are 28 pixels in each dimension.
img_h = img_w = 28
# Images are stored in one-dimensional arrays of this length.
img_size_flat = img_h * img_w
# Number of classes, one class for each of 10 digits.
n_classes = 10
# number of units in the first hidden layer
h1 = 200
# Graph:
# Like before, we start by constructing the graph. But, we need to define some functions that we need rapidly in our code.
# weight and bais wrappers
def weight_variable(name, shape):
"""
Create a weight variable with appropriate initialization
:param name: weight name
:param shape: weight shape
:return: initialized weight variable
"""
initer = tf.truncated_normal_initializer(stddev=0.01)
return tf.get_variable('W_' + name,
dtype=tf.float32,
shape=shape,
initializer=initer)
def bias_variable(name, shape):
"""
Create a bias variable with appropriate initialization
:param name: bias variable name
:param shape: bias variable shape
:return: initialized bias variable
"""
initial = tf.constant(0., shape=shape, dtype=tf.float32)
return tf.get_variable('b_' + name,
dtype=tf.float32,
initializer=initial)
def fc_layer(x, num_units, name, use_relu=True):
"""
Create a fully-connected layer
:param x: input from previous layer
:param num_units: number of hidden units in the fully-connected layer
:param name: layer name
:param use_relu: boolean to add ReLU non-linearity (or not)
:return: The output array
"""
with tf.variable_scope(name):
in_dim = x.get_shape()[1]
W = weight_variable(name, shape=[in_dim, num_units])
tf.summary.histogram('W', W)
b = bias_variable(name, [num_units])
tf.summary.histogram('b', b)
layer = tf.matmul(x, W)
layer += b
if use_relu:
layer = tf.nn.relu(layer)
return layer
# Now that we have our helper functions we can create our graph.
# Create graph
# Placeholders for inputs (x), outputs(y)
with tf.compat.v1.variable_scope('Input'):
x = tf.compat.v1.placeholder(tf.float32, shape=[None, img_size_flat], name='X')
tf.summary.image('input_image', tf.reshape(x, (-1, img_w, img_h, 1)), max_outputs=5)
y = tf.compat.v1.placeholder(tf.float32, shape=[None, n_classes], name='Y')
fc1 = fc_layer(x, h1, 'Hidden_layer', use_relu=True)
output_logits = fc_layer(fc1, n_classes, 'Output_layer', use_relu=False)
# Define the loss function, optimizer, and accuracy
with tf.compat.v1.variable_scope('Train'):
with tf.compat.v1.variable_scope('Loss'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output_logits), name='loss')
tf.summary.scalar('loss', loss)
with tf.compat.v1.variable_scope('Optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, name='Adam-op').minimize(loss)
with tf.compat.v1.variable_scope('Accuracy'):
correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name='correct_pred')
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
tf.summary.scalar('accuracy', accuracy)
# Network predictions
cls_prediction = tf.argmax(output_logits, axis=1, name='predictions')
# Initializing the variables
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
# Session:
# Launch the graph (session)
sess = tf.InteractiveSession() # using InteractiveSession instead of Session to test network in separate cell
sess.run(init)
train_writer = tf.summary.FileWriter(logs_path, sess.graph)
num_tr_iter = int(mnist.train.num_examples / batch_size)
global_step = 0
for epoch in range(epochs):
print('Training epoch: {}'.format(epoch + 1))
for iteration in range(num_tr_iter):
batch_x, batch_y = mnist.train.next_batch(batch_size)
global_step += 1
# Run optimization op (backprop)
feed_dict_batch = {x: batch_x, y: batch_y}
_, summary_tr = sess.run([optimizer, merged], feed_dict=feed_dict_batch)
train_writer.add_summary(summary_tr, global_step)
if iteration % display_freq == 0:
# Calculate and display the batch loss and accuracy
loss_batch, acc_batch = sess.run([loss, accuracy],
feed_dict=feed_dict_batch)
print("iter {0:3d}:\t Loss={1:.2f},\tTraining Accuracy={2:.01%}".
format(iteration, loss_batch, acc_batch))
# Run validation after every epoch
feed_dict_valid = {x: mnist.validation.images, y: mnist.validation.labels}
loss_valid, acc_valid = sess.run([loss, accuracy], feed_dict=feed_dict_valid)
print('---------------------------------------------------------')
print("Epoch: {0}, validation loss: {1:.2f}, validation accuracy: {2:.01%}".
format(epoch + 1, loss_valid, acc_valid))
print('---------------------------------------------------------')
我认为代码是为早期版本的 TensorFlow 设计的。我做了一些小的修改来让代码在我的笔记本电脑上运行。这是我正在努力解决的部分。
# Placeholders for inputs (x), outputs(y)
with tf.compat.v1.variable_scope('Input'):
x = tf.compat.v1.placeholder(tf.float32, shape=[None, img_size_flat], name='X')
tf.summary.image('input_image', tf.reshape(x, (-1, img_w, img_h, 1)), max_outputs=5)
y = tf.compat.v1.placeholder(tf.float32, shape=[None, n_classes], name='Y')
fc1 = fc_layer(x, h1, 'Hidden_layer', use_relu=True)
output_logits = fc_layer(fc1, n_classes, 'Output_layer', use_relu=False)
'with' 语句运行，但在这一行出现错误：
fc1 = fc_layer(x, h1, 'Hidden_layer', use_relu=True)
我认为更改为“tf.compat.v1”会解决不同 TensorFlow 版本的问题，但显然不是。
我在这里找到了代码示例。
https://www.easy-tensorflow.com/tf-tutorials/tensorboard/tb-embedding-visualization
【问题讨论】：
标签： python python-3.x tensorflow tensorflow2.0