【发布时间】：2018-11-02 10:38:42
【问题描述】：
错误信息：
NotFoundError：从检查点恢复失败。这很可能是由于检查点中缺少变量名称或其他图形键。请确保您没有根据检查点更改预期的图表。原始错误：
""" PLACEHOLDER """
x=tf.placeholder(tf.float32,shape=[None,784])
y_true=tf.placeholder(tf.float32,shape=[None,4])
Weight=tf.global_variables()
bias=tf.global_variables()
""" LAYERS:"""
"the image input layer 28*28 input"
x_image = tf.reshape(x,[-1,28,28,1])
"""first convolution, using 6*6 filter, and then max pooling 2by2, final
output will have depth 32
here we are calculating 32 features
"""
convo_1 = convolutional_layer(x_image,shape=[6,6,1,32])
convo_1_pooling = max_pool_2by2(convo_1)
"""first convolution, using 6*6 filter, and then max pooling 2by2, final
output will have 64
features hence depth 64
"""
convo_2 = convolutional_layer(convo_1_pooling,shape=[6,6,32,64])
convo_2_pooling = max_pool_2by2(convo_2)
"flattening the output of the last pooling layer to fuly connect it"
convo_2_flat = tf.reshape(convo_2_pooling,[-1,7*7*64])
Wt,bs=normal_full_layer(convo_2_flat,1024)#1024 nodes in the final layer
full_layer_one = tf.nn.relu(tf.matmul(convo_2_flat,Wt)+bs)
# NOTE THE PLACEHOLDER HERE!
hold_prob = tf.placeholder(tf.float32)
full_one_dropout = tf.nn.dropout(full_layer_one,keep_prob=hold_prob)
Weight, bias= normal_full_layer(full_one_dropout,2)
y_pred=tf.matmul(full_one_dropout,Weight)+bias
"""INITIALIZE VARIABLES"""
init = tf.global_variables_initializer()
"""Add ops to save and restore all the variables"""
saver = tf.train.Saver()
"""Later, launch the model, use the saver to restore variables from disk,
and
# do some work with the model"""
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess,SAVE_PATH)
print("Model restored.")
【问题讨论】：
-
这是调用保存模型的代码，因此在计算 y_pred 之后我没有用来计算损失函数（我已经删除了那部分代码）我只想要预测的 y 值数组对于我通过的输入 x 数组。
标签： python-3.x tensorflow conv-neural-network