qq_Q先生_0
2018-08-21 14:14
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'y' with dtype float and shape [?,10]
[[Node: y = Placeholder[dtype=DT_FLOAT, shape=[?,10], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'y', defined at:
File "E:/pycharmWorkspace/flaskDemo/app/mnist/convolutional.py", line 19, in <module>
y_ = tf.placeholder(tf.float32, [None, 10] , name='y');
19行代码 :
mnist = input_data.read_data_sets('MNIST_data', one_hot=True); #创建模型 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); y_ = tf.placeholder(tf.float32, [None, 10] , name='y');#(19行)报错行 cross_entropy = -tf.reduce_sum(y_ * tf.log(y)); train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy); #预测信息 correct_prediction = tf.equal(tf.argmax(y, 1) , tf.argmax(y_, 1)); #计算准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction , tf.float32)); saver = tf.train.Saver(variables); #训练 with tf.Session() as sess : merged_summary_op = tf.summary.merge_all(); summary_writer = tf.summary.FileWriter('/temp/mnsit_log/1' , sess.graph); #把图像加进来 summary_writer.add_graph(sess.graph); sess.run(tf.global_variables_initializer()); #训练 for i in range(20000) : batch = mnist.train.next_batch(50); #每隔100次 if i% 100 == 0: train_accuracy = accuracy.eval(feed_dict={x:batch[0] , y:batch[1] , keep_prob : 1.0}); print('step : %d <==> train_accuracy %g: ' %(i , train_accuracy)) sess.run(train_step , feed_dict={x:batch[0] , y_:batch[1] , keep_prob : 0.5}); print("卷积正确率 : " ,sess.run(accuracy , feed_dict={x:mnist.test.images , y_:mnist.test.labels, keep_prob : 1.0}) ); path = saver.save(sess, os.path.join(os.path.dirname(__file__) , 'data' , 'convolutional.ckpt'),write_meta_graph=False , write_state=False); print('saverd :',path); sess.close();
if i% 100 == 0: train_accuracy = accuracy.eval(feed_dict={x:batch[0] , y:batch[1] , keep_prob : 1.0});
这个里面应该是y_
TensorFlow与Flask结合打造手写体数字识别
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