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我有另一个TensorFlow查询:TensorFlow:不是所有我的变量都被恢复 - Python的
我训练回归模型,节省了重量和偏见,然后将它们恢复到重新运行在不同的数据模型组。至少,这就是我想要做的。并非我所有的体重都正在恢复。下面是用于保存我的变量代码:
# Add ops to save and restore all the variables.
saver = tf.train.Saver({**weights, **biases})
# Save the variables to disk.
save_path = saver.save(sess, "Saved_Vars.ckpt")
这是我的恢复和运行模式整个代码:
# Network Parameters
n_hidden_1 = 9
n_hidden_2 = 56
n_hidden_3 = 8
n_input = 9
n_classes = 1
# TensorFlow Graph Input
x = tf.placeholder("float", [None, n_input])
# Create Multilayer Model
def multilayer_perceptron(x, weights, biases):
# First hidden layer with RELU activation
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
# Second hidden layer with RELU activation
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
# Second hidden layer with RELU activation
layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
layer_3 = tf.nn.relu(layer_3)
# Last output layer with linear activation
out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
return out_layer
# weights and biases
weights = {
'h1': tf.Variable(tf.zeros([n_input, n_hidden_1])),
'h2': tf.Variable(tf.zeros([n_hidden_1, n_hidden_2])),
'h3': tf.Variable(tf.zeros([n_hidden_2, n_hidden_3])),
'out': tf.Variable(tf.zeros([n_hidden_3, n_classes]))
}
biases = {
'b1' : tf.Variable(tf.zeros([n_hidden_1])),
'b2': tf.Variable(tf.zeros([n_hidden_2])),
'b3': tf.Variable(tf.zeros([n_hidden_3])),
'out': tf.Variable(tf.zeros([n_classes]))
}
# Construct Model
pred = multilayer_perceptron(x, weights, biases)
pred = tf.transpose(pred)
# Initialize variables
init = tf.global_variables_initializer()
# RUNNING THE SESSION
# launch the session
sess = tf.InteractiveSession()
# Initialize all the variables
sess.run(init)
# Add ops to save and restore all the variables.
saver = tf.train.Saver({**weights, **biases})
# Restore variables from disk.
saver.restore(sess, "Saved_Vars.ckpt")
# Use the restored model to predict the target values
prediction = sess.run(pred, feed_dict={x:dataVar_scaled}) #pred.eval(feed_dict={x:X})
现在,这里是有我困惑/沮丧/恼火。从权重我可以恢复'h1','h2'和'h3',但不是'出'。为什么不'出'?有什么我做错了吗?请你花几分钟时间来帮助我?
非常感谢
我直接在Windows 10上运行的Python 3.5和0.12 TensorFlow和我使用Spyder的IDE。
谢谢你这么多,改变变量名工作一种享受! :)请你能解释一下,“{**权重,**偏差}”在做什么,因为我显然没有很好的把握。再次感谢 – jlt199
这[SO线程](http://stackoverflow.com/questions/36901/what-does-double-star-and-star-do-for-parameters)是相当不错的。另外,Saver真的想要一个列表,而不是一本字典。我通常不会将任何值传递给Saver,然后默认保存任何可以保存的值。 –