TensorFlow模型和权重的保存
因为大肥狼在使用保存的模型和权重进行预测时遇到了一些问题，所以本文将介绍如何在TensorFlow中保存模型和权重，并如何使用保存的模型和权重来进行预测。
1.代码
我们的代码主要是吴恩达作业第二单元第三周-----tensorflow入门这一部分。
这部分比较简单
主要是使用tf.train.Saver()来保存神经网络的网络结构图和相关变量。这里我新建了一个checkpoint文件夹用来保存模型和权重文件。
使用的代码如下
saver = tf.train.Saver()Tensorflow变量的作用范围是在一个session里面。在保存模型的时候，应该在session里面通过save方法保存。其中sess是会话名称，./checkpoint/model.ckpt是你保存路径下模型名字。
saved_path = saver.save(sess,'./checkpoint/model.ckpt' )完整代码
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
num_epochs = 1500, minibatch_size = 32, print_cost = True):
ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
tf.set_random_seed(1) # to keep consistent results
seed = 3 # to keep consistent results
(n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)
n_y = Y_train.shape[0] # n_y : output size
costs = [] # To keep track of the cost
# Create Placeholders of shape (n_x, n_y)
X, Y = create_placeholders(n_x, n_y)
# Initialize parameters
parameters = initialize_parameters()
# Forward propagation: Build the forward propagation in the tensorflow graph
Z3 = forward_propagation(X, parameters)
tf.add_to_collection('pred_network', Z3)
# Cost function: Add cost function to tensorflow graph
cost = compute_cost(Z3, Y)
# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
# Initialize all the variables
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# Start the session to compute the tensorflow graph
with tf.Session() as sess:
# Run the initialization
sess.run(init)
# Do the training loop
for epoch in range(num_epochs):
epoch_cost = 0. # Defines a cost related to an epoch
num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
for minibatch in minibatches:
# Select a minibatch
(minibatch_X, minibatch_Y) = minibatch
# IMPORTANT: The line that runs the graph on a minibatch.
# Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
_ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
epoch_cost += minibatch_cost / num_minibatches
# Print the cost every epoch
if print_cost == True and epoch % 100 == 0:
print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
if print_cost == True and epoch % 5 == 0:
costs.append(epoch_cost)
#保存模型
saved_path = saver.save(sess,'./checkpoint/model.ckpt' )
# plot the cost
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
# lets save the parameters in a variable
parameters = sess.run(parameters)
print ("Parameters have been trained!")
# Calculate the correct predictions
correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))
# Calculate accuracy on the test set
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print ("Train Accuracy:", accuracy.eval（{X: X_train, Y: Y_train}))
print ("Test Accuracy:", accuracy.eval（{X: X_test, Y: Y_test}))
return parameters这里的例子比较复杂，包括你也可以设置每100代保存一次权重
保存完后，你的文件目录下的checkpoint 文件夹下会多出四个文件。如下图所示
model.meta为模型文件，model.data-00000-of-00001为数据文件。后面我们恢复模型需要用到。
使用模型和权重进行预测
接下来，我们讲预测时如何使用模型和权重进行预测。可以参考这个链接 ，写的很简洁。
1.导入保存好的模型结构，即model.meta文件
meta_path = './checkpoint/model.ckpt.meta'
saver = tf.train.import_meta_graph(meta_path) # 导入2.加载的（变量）参数：
使用restore()方法恢复模型的变量参数。
data_path = './checkpoint/model.ckpt'
saver.restore(sess,data_path) # 导入变量值也可以使用
data_path = './checkpoint/'
saver.restore(sess,tf.train.latest_checkpoint(data_path)) # 导入变量值注意这里 data_path 里面的路径： ‘./checkpoint/model.ckpt’
3.使用graph.get_tensor_by_name()根据张量名称获取你想要的张量。因为我在训练的时候设置占位符时，将我的输入张量名字定为x，如下图所示。
def create_placeholders(n_x, n_y):
X = tf.placeholder(tf.float32, shape=[n_x, None],name="x")
Y = tf.placeholder(tf.float32, shape=[n_y, None],name="y")
return X, Y所以恢复输入张量
x=graph.get_operation_by_name('x').outputs[0]3.因为前向传播过程中，没有为我们计算的输出y_hat设置张量。前向传播中，我们只计算到z3，所以我们用tf.add_to_collection() 来加载一下你预测要用的参数。这里我需要Z3，我就在训练代码中收集Z3,并给他命名为’pred_network’。
Z3 = forward_propagation(X, parameters)
tf.add_to_collection('pred_network', Z3)然后预测时恢复
z3=tf.get_collection("pred_network")[0]然后在会话中计算z3
z3=sess.run(z3, feed_dict={x:my_image})完整代码如下
import numpy as np
import tensorflow as tf
import scipy
from PIL import Image
from scipy import ndimage
import matplotlib.pyplot as plt
fname = "images/thumbs_up.jpg"
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64,64)).reshape((1, 64*64*3)).T
with tf.Session() as sess:
meta_path = './checkpoint/model.ckpt.meta'
model_path = './checkpoint/model.ckpt'
saver = tf.train.import_meta_graph(meta_path) # 导入图
saver.restore(sess,model_path) # 导入变量值
graph = tf.get_default_graph()
x=graph.get_operation_by_name('x').outputs[0]
print(x)
z3=tf.get_collection("pred_network")[0]
z3=sess.run(z3, feed_dict={x:my_image})
y=np.argmax(z3)
print(str(np.squeeze(y)))这样就完成预测啦。如果用jupyter notebook在预测的时候报没有数据喂入的错，可能重启一下jupyter就好了。
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