存储图和训练好的权重

（1）tensorflow存储图和训练好的权重

from __future__ import absolute_import, unicode_literalsimport input_dataimport tensorflow as tfimport shutilimport os.pathexport_dir = './tmp/expert-export'if os.path.exists(export_dir):    shutil.rmtree(export_dir)def weight_variable(shape):    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)def conv2d(x, W):    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x):    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],                          strides=[1, 2, 2, 1], padding='SAME')mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)g = tf.Graph()with g.as_default():    x = tf.placeholder("float", shape=[None, 784])    y_ = tf.placeholder("float", shape=[None, 10])    W_conv1 = weight_variable([5, 5, 1, 32])    b_conv1 = bias_variable([32])    x_image = tf.reshape(x, [-1, 28, 28, 1])    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)    h_pool1 = max_pool_2x2(h_conv1)    W_conv2 = weight_variable([5, 5, 32, 64])    b_conv2 = bias_variable([64])    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)    h_pool2 = max_pool_2x2(h_conv2)    W_fc1 = weight_variable([7 * 7 * 64, 1024])    b_fc1 = bias_variable([1024])    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)    keep_prob = tf.placeholder("float")    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)    W_fc2 = weight_variable([1024, 10])    b_fc2 = bias_variable([10])    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    sess = tf.Session()    sess.run(tf.initialize_all_variables())    for i in range(201):        batch = mnist.train.next_batch(50)        if i % 100 == 0:            train_accuracy = accuracy.eval(                {x: batch[0], y_: batch[1], keep_prob: 1.0}, sess)            print "step %d, training accuracy %g" % (i, train_accuracy)        train_step.run(            {x: batch[0], y_: batch[1], keep_prob: 0.5}, sess)    print "test accuracy %g" % accuracy.eval(        {x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}, sess)# Store variable_W_conv1 = W_conv1.eval(sess)_b_conv1 = b_conv1.eval(sess)_W_conv2 = W_conv2.eval(sess)_b_conv2 = b_conv2.eval(sess)_W_fc1 = W_fc1.eval(sess)_b_fc1 = b_fc1.eval(sess)_W_fc2 = W_fc2.eval(sess)_b_fc2 = b_fc2.eval(sess)sess.close()# Create new graph for exportingg_2 = tf.Graph()with g_2.as_default():    x_2 = tf.placeholder("float", shape=[None, 784], name="input")    W_conv1_2 = tf.constant(_W_conv1, name="constant_W_conv1")    b_conv1_2 = tf.constant(_b_conv1, name="constant_b_conv1")    x_image_2 = tf.reshape(x_2, [-1, 28, 28, 1])    h_conv1_2 = tf.nn.relu(conv2d(x_image_2, W_conv1_2) + b_conv1_2)    h_pool1_2 = max_pool_2x2(h_conv1_2)    W_conv2_2 = tf.constant(_W_conv2, name="constant_W_conv2")    b_conv2_2 = tf.constant(_b_conv2, name="constant_b_conv2")    h_conv2_2 = tf.nn.relu(conv2d(h_pool1_2, W_conv2_2) + b_conv2_2)    h_pool2_2 = max_pool_2x2(h_conv2_2)        W_fc1_2 = tf.constant(_W_fc1, name="constant_W_fc1")    b_fc1_2 = tf.constant(_b_fc1, name="constant_b_fc1")    h_pool2_flat_2 = tf.reshape(h_pool2_2, [-1, 7 * 7 * 64])    h_fc1_2 = tf.nn.relu(tf.matmul(h_pool2_flat_2, W_fc1_2) + b_fc1_2)    W_fc2_2 = tf.constant(_W_fc2, name="constant_W_fc2")    b_fc2_2 = tf.constant(_b_fc2, name="constant_b_fc2")    # DropOut is skipped for exported graph.        y_conv_2 = tf.nn.softmax(tf.matmul(h_fc1_2, W_fc2_2) + b_fc2_2, name="output")        sess_2 = tf.Session()    init_2 = tf.initialize_all_variables();    sess_2.run(init_2)    graph_def = g_2.as_graph_def()    tf.train.write_graph(graph_def, export_dir, 'expert-graph.pb', as_text=False)    # Test trained model    y__2 = tf.placeholder("float", [None, 10])    correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1))    accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float"))    print "check accuracy %g" % accuracy_2.eval(        {x_2: mnist.test.images, y__2: mnist.test.labels}, sess_2)运行结果/usr/bin/python2.7 /home/acer/PycharmProjects/trainer-script-minist/expert.pyExtracting ./tmp/data/train-images-idx3-ubyte.gzExtracting ./tmp/data/train-labels-idx1-ubyte.gzExtracting ./tmp/data/t10k-images-idx3-ubyte.gzExtracting ./tmp/data/t10k-labels-idx1-ubyte.gzstep 0, training accuracy 0.1step 100, training accuracy 0.84step 200, training accuracy 0.94test accuracy 0.8998check accuracy 0.8998Process finished with exit code 0

读取存储的.pb并使用

#encoding:uft-8#读取存储的图，可运行from __future__ import absolute_import, unicode_literalsimport input_dataimport tensorflow as tfimport shutilimport os.pathmnist = input_data.read_data_sets("./tmp/data/", one_hot=True)# produces the expected result.x_2 = tf.placeholder("float", shape=[None, 784], name="input")y__2 = tf.placeholder("float", [None, 10])with tf.Graph().as_default():    output_graph_def = tf.GraphDef()    output_graph_path = './tmp/expert-export/expert-graph.pb'    #sess.graph.add_to_collection("input", mnist.test.images)    with open(output_graph_path, "rb") as f:        output_graph_def.ParseFromString(f.read())        _ = tf.import_graph_def(output_graph_def, name="")    with tf.Session() as sess:        tf.initialize_all_variables().run()        input_x = sess.graph.get_tensor_by_name("input:0")        print input_x        output = sess.graph.get_tensor_by_name("output:0")        print output        y_conv_2 = sess.run(output,{input_x:mnist.test.images})        print "y_conv_2", y_conv_2        # Test trained model        #y__2 = tf.placeholder("float", [None, 10])        y__2 = mnist.test.labels;        correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1))        print "correct_prediction_2", correct_prediction_2        accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float"))        print "accuracy_2", accuracy_2        print "check accuracy %g" % accuracy_2.eval()运行结果：

/usr/bin/python2.7 /home/acer/PycharmProjects/trainer-script-minist/testexpert.py
Extracting ./tmp/data/train-images-idx3-ubyte.gz
Extracting ./tmp/data/train-labels-idx1-ubyte.gz
Extracting ./tmp/data/t10k-images-idx3-ubyte.gz
Extracting ./tmp/data/t10k-labels-idx1-ubyte.gz
Tensor("input:0", dtype=float32)
Tensor("output:0", shape=(?, 10), dtype=float32)
y_conv_2 [[  1.37317020e-05   2.21044629e-05   7.19948293e-05 ...,   9.98878419e-01
    2.16295721e-05   5.87339920e-04]
 [  5.59040019e-03   1.49921319e-02   7.76712596e-01 ...,   2.77250329e-05
    7.95767531e-02   1.50265405e-04]
 [  1.17397300e-04   9.85014021e-01   5.27875684e-03 ...,   1.45449198e-03
    2.34977412e-03   2.11255602e-03]
 ..., 
 [  1.45151716e-04   4.11947171e-04   1.44378588e-04 ...,   1.72489304e-02
    7.79939666e-02   1.54636368e-01]
 [  6.98981108e-03   1.21029736e-02   1.06854215e-02 ...,   1.07497610e-02
    3.34680617e-01   1.42563693e-02]
 [  8.54243699e-04   7.11486928e-06   4.57449147e-04 ...,   3.66224917e-08
    3.96781206e-06   8.27823987e-06]]
correct_prediction_2 Tensor("Equal:0", shape=(10000,), dtype=bool)
accuracy_2 Tensor("Mean:0", shape=(), dtype=float32)
check accuracy 0.899