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@@ -3,12 +3,14 @@
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# In[2]:
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# In[2]:
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+from nt import chdir
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+mdir="C:/Users/dell/workspace/firstPython/mnist"
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+chdir(mdir)
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+
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import tensorflow as tf
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import tensorflow as tf
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import numpy as np
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import numpy as np
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import input_data
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import input_data
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-from nt import chdir
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-chdir("C:/Users/dell/workspace/firstPython/mnist")
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mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
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mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
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@@ -16,6 +18,9 @@ mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
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#启动.Tensorflow依赖于一个高效的C++后端来进行计算。与后端的这个连接叫做session。
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#启动.Tensorflow依赖于一个高效的C++后端来进行计算。与后端的这个连接叫做session。
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sess = tf.InteractiveSession()
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sess = tf.InteractiveSession()
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+#TensorBoard读取的log文件
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+file_writer = tf.summary.FileWriter('%s%s' % (mdir,'/mnist_logs'), sess.graph)
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+
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#占位符
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#占位符
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x = tf.placeholder("float", shape=[None, 784])
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x = tf.placeholder("float", shape=[None, 784])
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y_ = tf.placeholder("float", shape=[None, 10])
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y_ = tf.placeholder("float", shape=[None, 10])
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@@ -96,7 +101,7 @@ sess.run(tf.initialize_all_variables())
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# In[8]:
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# In[8]:
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#for i in range(20000):
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#for i in range(20000):
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-for i in range(1000):
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+for i in range(100):
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batch = mnist.train.next_batch(50)
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batch = mnist.train.next_batch(50)
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if i%100 == 0:
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if i%100 == 0:
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train_accuracy = accuracy.eval(feed_dict={
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train_accuracy = accuracy.eval(feed_dict={
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