我tensorflow模型如下:如何保存tensorflow模型(省略标签张量),没有定义
X = tf.placeholder(tf.float32, [None,training_set.shape[1]],name = 'X')
Y = tf.placeholder(tf.float32,[None,training_labels.shape[1]], name = 'Y')
A1 = tf.contrib.layers.fully_connected(X, num_outputs = 50, activation_fn = tf.nn.relu)
A1 = tf.nn.dropout(A1, 0.8)
A2 = tf.contrib.layers.fully_connected(A1, num_outputs = 2, activation_fn = None)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = A2, labels = Y))
global_step = tf.Variable(0, trainable=False)
start_learning_rate = 0.001
learning_rate = tf.train.exponential_decay(start_learning_rate, global_step, 200, 0.1, True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
现在我要救这个模型遗漏张Y
(Y
是标签张量对于培训,X
是实际的输入)。同时在提及freeze_graph.py
时提及输出节点时,我应该提及"A2"
还是以其他名称保存?
感谢@Maxim的回复。真的很感谢你的时间。 –