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Update training_and_val.py

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Lines changed: 8 additions & 7 deletions

Original file line number	Diff line number	Diff line change
`@@ -55,15 +55,16 @@ def train():`
`55`	`55`	`is_train=False,`
`56`	`56`	`batch_size= BATCH_SIZE,`
`57`	`57`	`shuffle=False)`
	`58`	`+`
	`59`	`+ x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])`
	`60`	`+ y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE, N_CLASSES])`
`58`	`61`
`59`		`- logits = VGG.VGG16N(tra_image_batch, N_CLASSES, IS_PRETRAIN)`
`60`		`- loss = tools.loss(logits, tra_label_batch)`
`61`		`- accuracy = tools.accuracy(logits, tra_label_batch)`
`62`		`- my_global_step = tf.Variable(0, name='global_step', trainable=False)`
`63`		`- train_op = tools.optimize(loss, learning_rate, my_global_step)`
	`62`	`+ logits = VGG.VGG16N(x, N_CLASSES, IS_PRETRAIN)`
	`63`	`+ loss = tools.loss(logits, y_)`
	`64`	`+ accuracy = tools.accuracy(logits, y_)`
`64`	`65`
`65`		`- x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])`
`66`		`- y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE, N_CLASSES])`
	`66`	`+ my_global_step = tf.Variable(0, name='global_step', trainable=False)`
	`67`	`+ train_op = tools.optimize(loss, learning_rate, my_global_step)`
`67`	`68`
`68`	`69`	`saver = tf.train.Saver(tf.global_variables())`
`69`	`70`	`summary_op = tf.summary.merge_all()`

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