WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

Instructions for updating:

Please use alternatives such as official/mnist/dataset.py from tensorflow/models.

Traceback (most recent call last):

? File "D:/code/PycharmProject/mnist_testdemo2/mnist/convolutional.py", line 12, in <module>

? ? y, variables = model.convolutional(x, keep_prob)

? File "D:\code\PycharmProject\mnist_testdemo2\mnist\model.py", line 24, in convolutional

? ? W_conv1 = weight_variable([5, 5, 1, 32])

? File "D:\code\PycharmProject\mnist_testdemo2\mnist\model.py", line 17, in weight_variable

? ? initial = tf.truncated.normal(shape, stddev=0.1)

AttributeError: module 'tensorflow' has no attribute 'truncated'

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我的model文件代碼：

import?tensorflow?as?tf

#?線性模型?Y=W*x?+?b
def?regressions(x):
????W?=?tf.Variable(tf.zeros([784,?10]),?name="W")
????b?=?tf.Variable(tf.zeros([10]),?name='b')
????y?=?tf.nn.softmax(tf.matmul(x,?W)?+?b)
????return?y,?[W,b]

#?卷積模型
def?convolutional(x,?keep_prob):
????def?conv2d(x,?W):
????????return?tf.nn.conv2d([1,?1,?1,?1],?padding='SAME')
????def?max_pool_2x2(x):
????????return?tf.nn.max_pool(x,?ksize=[1,?2,?2,?1],?strides=[1,?2,?2,?1])
????def?weight_variable(shape):
????????initial?=?tf.truncated.normal(shape,?stddev=0.1)
????????return?tf.Variable(initial)
????def?bias_variable(shape):
????????initial?=?tf.constant(0.1,?shape=shape)
????????return?tf.Variable(initial)

????x_image?=?tf.reshape(x,?[-1,?28,?28,?1])
????W_conv1?=?weight_variable([5,?5,?1,?32])
????b_conv1?=?bias_variable([32])
????h_conv1?=?tf.nn.relu(conv2d(x_image,?W_conv1)?+?b_conv1)
????h_pool1?=?max_pool_2x2(h_conv1)

????W_conv2?=?weight_variable([5,?5,?32,?64])
????b_conv2?=?bias_variable([64])
????h_conv2?=?tf.nn.relu(conv2d(h_pool1,?W_conv2)?+?b_conv2)
????h_pool2?=?max_pool_2x2(h_conv2)

????#?full?connection
????W_fc1?=?weight_variable([7?*?7?*?64,?1024])
????b_fc1?=?bias_variable([1024])
????h_pool2_flat?=?tf.reshape(h_pool2,?[-1,?7?*?7?*?64])
????h_fc1?=?tf.nn.relu(tf.matmul(h_pool2_flat,?W_fc1)?+?b_fc1)

????h_fc1_drop?=?tf.nn.dropout(h_fc1,?keep_prob)

????W_fc2?=?weight_variable([1024,?10])
????b_fc2?=?bias_variable([10])
????y?=?tf.nn.softmax(tf.matmul(h_fc1_drop,?W_fc2)?+?b_fc2)

????return?y,?[W_conv1,?b_conv1,?W_conv2,?b_conv2,?W_fc1,?b_fc1,?W_fc2,?b_fc2]

我的convolutional代碼：

import?os
import?model
import?tensorflow?as?tf
import?input_data

data?=?input_data.read_data_sets('MNIST_data',?one_hot=True)

#model
with?tf.variable_scope("convolutional"):
????x?=?tf.placeholder(tf.float32,?[None,?784],?name='x')
????keep_prob?=?tf.placeholder(tf.float32)
????y,?variables?=?model.convolutional(x,?keep_prob)

#train
y_?=?tf.placeholder(tf.float32,?[None,?10],?name='y')
cross_entropy?=?-tf.reduce_sum(y_?*?tf.log(y))
train_step?=?tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction?=?tf.equal(tf.argmax(y,?1),?tf.argmax(y_,?1))
accuracy?=?tf.reduce_mean(tf.cast(correct_prediction,?tf.float32))

saver?=?tf.train.Saver(variables)

with?tf.Session()?as?sess:
????merged_summary_op?=?tf.summary.merge_all()
????summay_writer?=?tf.summary.FileWriter('./tem/mnist_log/1',?sess.graph)
????summay_writer.add_graph(sess.graph)
????sess.run(tf.lobal_variables_initializer())

????#最好做兩萬次訓(xùn)練
????for?i?in?range(2000):
????????batch?=?data.train.next_batch(50)
????????if(i?%?100?==?0):
????????????train_accuracy?=?accuracy.eval(feed_dict={x:?batch[0],?y_:?batch[1],?keep_prob:?1.0})
????????????print("step?%d,?training?accuracy?%g"?%?(i,?train_accuracy))
????????sess.run(train_step,?feed_dict={x:?batch[0],?y_:?batch[1],?keep_prob:0.5})

????print(sess.run(accuracy,?feed_dict={x:?data.test.images,?y_:?data.test.labels,?keep_prob:?1.0}))
????path?=?saver.save(sess,?os.path.join(os.path.dirname(__file__),?'data',?'convalutional.ckpt',?write_meta_graph=False,?write_state=False))
????print("Saved:",?path)