python - AttributeError:模块“张量流”没有属性“层”
问题描述
我正在尝试实现 VGG,但遇到了上述奇怪的错误。我在 Ubuntu 上运行 TFv2。这可能是因为我没有运行 CUDA 吗?
代码来自这里。
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)
解决方案
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