Python 属性错误：模块'；tensorflow'；没有属性'；层'；

我试图实现VGG，但得到了上述奇怪的错误。我正在Ubuntu上运行TFv2。这可能是因为我没有管理CUDA吗

代码来自

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您正在使用的代码是用Tensorflow v1.x编写的，与Tensorflow v2不兼容。最简单的解决方案可能是降级到tensorflow v1的版本，以按原样运行代码

另一个选择是将代码从v1迁移到v2

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第三种选择是使用

tf.compat

模块来获得一些复古兼容性。例如，

tf.layers

在Tensorflow v2中不再存在。您可以改用

tf.compat.v1.layers

（例如，请参见函数），但这是一个临时修复，因为这些函数将在将来的版本中删除

使用tensorflow 1.x而不是tensorflow 2.x版本。但请记住，Python3.8上没有2.x版本。使用具有tensorflow 1.x的Python的较低版本

python3.6-m pip安装tensorflow==1.8.0

对tensorflow了解不多，但可能是因为您将tensorflow声明为tf，所以为什么不尝试从tf.keras.layers import中执行

。

或者从tensorflow.python.keras.layers import中执行类似的

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Imports

import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# tf.logging.set_verbosity(tf.logging.INFO)

from tensorflow.keras.layers import Conv2D, Dense, Flatten

np.random.seed(1)

mnist = tf.keras.datasets.mnist

(train_data, train_labels), (eval_data, eval_labels) = mnist.load_data()

train_data, train_labels = train_data / 255.0, train_labels / 255.0

# Add a channels dimension
train_data = train_data[..., tf.newaxis]
train_labels = train_labels[..., tf.newaxis]

index = 7
plt.imshow(train_data[index].reshape(28, 28))
plt.show()
time.sleep(5);
print("y = " + str(np.squeeze(train_labels[index])))

print ("number of training examples = " + str(train_data.shape[0]))
print ("number of evaluation examples = " + str(eval_data.shape[0]))
print ("X_train shape: " + str(train_data.shape))
print ("Y_train shape: " + str(train_labels.shape))
print ("X_test shape: " + str(eval_data.shape))
print ("Y_test shape: " + str(eval_labels.shape))

print("done")

def cnn_model_fn(features, labels, mode):
    # Input Layer
    input_height, input_width = 28, 28
    input_channels = 1
    input_layer = tf.reshape(features["x"], [-1, input_height, input_width, input_channels])

    # Convolutional Layer #1 and Pooling Layer #1
    conv1_1 = tf.layers.conv2d(inputs=input_layer, filters=64, kernel_size=[3, 3], padding="same",
                               activation=tf.nn.relu)
    conv1_2 = tf.layers.conv2d(inputs=conv1_1, filters=64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #2 and Pooling Layer #2
    conv2_1 = tf.layers.conv2d(inputs=pool1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv2_2 = tf.layers.conv2d(inputs=conv2_1, filters=128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #3 and Pooling Layer #3
    conv3_1 = tf.layers.conv2d(inputs=pool2, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv3_2 = tf.layers.conv2d(inputs=conv3_1, filters=256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool3 = tf.layers.max_pooling2d(inputs=conv3_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #4 and Pooling Layer #4
    conv4_1 = tf.layers.conv2d(inputs=pool3, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv4_2 = tf.layers.conv2d(inputs=conv4_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool4 = tf.layers.max_pooling2d(inputs=conv4_2, pool_size=[2, 2], strides=2, padding="same")

    # Convolutional Layer #5 and Pooling Layer #5
    conv5_1 = tf.layers.conv2d(inputs=pool4, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    conv5_2 = tf.layers.conv2d(inputs=conv5_1, filters=512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu)
    pool5 = tf.layers.max_pooling2d(inputs=conv5_2, pool_size=[2, 2], strides=2, padding="same")

    # FC Layers
    pool5_flat = tf.contrib.layers.flatten(pool5)
    FC1 = tf.layers.dense(inputs=pool5_flat, units=4096, activation=tf.nn.relu)
    FC2 = tf.layers.dense(inputs=FC1, units=4096, activation=tf.nn.relu)
    FC3 = tf.layers.dense(inputs=FC2, units=1000, activation=tf.nn.relu)

    """the training argument takes a boolean specifying whether or not the model is currently 
    being run in training mode; dropout will only be performed if training is true. here, 
    we check if the mode passed to our model function cnn_model_fn is train mode. """
    dropout = tf.layers.dropout(inputs=FC3, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

    # Logits Layer or the output layer. which will return the raw values for our predictions.
    # Like FC layer, logits layer is another dense layer. We leave the activation function empty
    # so we can apply the softmax
    logits = tf.layers.dense(inputs=dropout, units=10)

    # Then we make predictions based on raw output
    predictions = {
        # Generate predictions (for PREDICT and EVAL mode)
        # the predicted class for each example - a vlaue from 0-9
        "classes": tf.argmax(input=logits, axis=1),
        # to calculate the probablities for each target class we use the softmax
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }

    # so now our predictions are compiled in a dict object in python and using that we return an estimator object
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    '''Calculate Loss (for both TRAIN and EVAL modes): computes the softmax entropy loss. 
    This function both computes the softmax activation function as well as the resulting loss.'''
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    # Configure the Training Options (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())

        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels,
                                        predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(mode=mode,
                                      loss=loss,
                                      eval_metric_ops=eval_metric_ops)

print("done2")

mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
                                          model_dir="/tmp/mnist_vgg13_model")
print("done3")
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(x={"x": train_data},
                                                        y=train_labels,
                                                        batch_size=100,
                                                        num_epochs=100,
                                                        shuffle=True)

print("done4")
mnist_classifier.train(input_fn=train_input_fn,
                       steps=None,
                       hooks=None)
print("done5")
eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data},
                                                   y=eval_labels,
                                                   num_epochs=1,
                                                   shuffle=False)
print("done6")
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)

print(eval_results)