MNIST例子构建tensorflow Android应用

和谷歌给的例子差异，是这里给出了如何生成所需要的pb文件：

https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample

生成PB模型文件python mnist.py
mnist.py

from __future__ import print_functionimport shutilimport os.pathimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataEXPORT_DIR = './model'if os.path.exists(EXPORT_DIR):    shutil.rmtree(EXPORT_DIR)mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# Parameterslearning_rate = 0.001training_iters = 200000batch_size = 128display_step = 10# Network Parametersn_input = 784  # MNIST data input (img shape: 28*28)n_classes = 10  # MNIST total classes (0-9 digits)dropout = 0.75  # Dropout, probability to keep units# tf Graph inputx = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])keep_prob = tf.placeholder(tf.float32)  # dropout (keep probability)# Create some wrappers for simplicitydef conv2d(x, W, b, strides=1):    # Conv2D wrapper, with bias and relu activation    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')    x = tf.nn.bias_add(x, b)    return tf.nn.relu(x)def maxpool2d(x, k=2):    # MaxPool2D wrapper    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],                          padding='SAME')# Create Modeldef conv_net(x, weights, biases, dropout):    # Reshape input picture    x = tf.reshape(x, shape=[-1, 28, 28, 1])    # Convolution Layer    conv1 = conv2d(x, weights['wc1'], biases['bc1'])    # Max Pooling (down-sampling)    conv1 = maxpool2d(conv1, k=2)    # Convolution Layer    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])    # Max Pooling (down-sampling)    conv2 = maxpool2d(conv2, k=2)    # Fully connected layer    # Reshape conv2 output to fit fully connected layer input    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])    fc1 = tf.nn.relu(fc1)    # Apply Dropout    fc1 = tf.nn.dropout(fc1, dropout)    # Output, class prediction    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])    return out# Store layers weight & biasweights = {    # 5x5 conv, 1 input, 32 outputs    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),    # 5x5 conv, 32 inputs, 64 outputs    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),    # fully connected, 7*7*64 inputs, 1024 outputs    'wd1': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),    # 1024 inputs, 10 outputs (class prediction)    'out': tf.Variable(tf.random_normal([1024, n_classes]))}biases = {    'bc1': tf.Variable(tf.random_normal([32])),    'bc2': tf.Variable(tf.random_normal([64])),    'bd1': tf.Variable(tf.random_normal([1024])),    'out': tf.Variable(tf.random_normal([n_classes]))}# Construct modelpred = conv_net(x, weights, biases, keep_prob)# Define loss and optimizercost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate modelcorrect_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initializing the variablesinit = tf.initialize_all_variables()# Launch the graphwith tf.Session() as sess:    sess.run(init)    step = 1    # Keep training until reach max iterations    while step * batch_size < training_iters:        batch_x, batch_y = mnist.train.next_batch(batch_size)        # Run optimization op (backprop)        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,                                       keep_prob: dropout})        if step % display_step == 0:            # Calculate batch loss and accuracy            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,                                                              y: batch_y,                                                              keep_prob: 1.})            print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \                  "{:.6f}".format(loss) + ", Training Accuracy= " + \                  "{:.5f}".format(acc))        step += 1    print("Optimization Finished!")    # Calculate accuracy for 256 mnist test images    print("Testing Accuracy:", \          sess.run(accuracy, feed_dict={x: mnist.test.images[:256],                                        y: mnist.test.labels[:256],                                        keep_prob: 1.}))    WC1 = weights['wc1'].eval(sess)    BC1 = biases['bc1'].eval(sess)    WC2 = weights['wc2'].eval(sess)    BC2 = biases['bc2'].eval(sess)    WD1 = weights['wd1'].eval(sess)    BD1 = biases['bd1'].eval(sess)    W_OUT = weights['out'].eval(sess)    B_OUT = biases['out'].eval(sess)# Create new graph for exportingg = tf.Graph()with g.as_default():    x_2 = tf.placeholder("float", shape=[None, 784], name="input")    WC1 = tf.constant(WC1, name="WC1")    BC1 = tf.constant(BC1, name="BC1")    x_image = tf.reshape(x_2, [-1, 28, 28, 1])    CONV1 = conv2d(x_image, WC1, BC1)    MAXPOOL1 = maxpool2d(CONV1, k=2)    WC2 = tf.constant(WC2, name="WC2")    BC2 = tf.constant(BC2, name="BC2")    CONV2 = conv2d(MAXPOOL1, WC2, BC2)    MAXPOOL2 = maxpool2d(CONV2, k=2)    WD1 = tf.constant(WD1, name="WD1")    BD1 = tf.constant(BD1, name="BD1")    FC1 = tf.reshape(MAXPOOL2, [-1, WD1.get_shape().as_list()[0]])    FC1 = tf.add(tf.matmul(FC1, WD1), BD1)    FC1 = tf.nn.relu(FC1)    W_OUT = tf.constant(W_OUT, name="W_OUT")    B_OUT = tf.constant(B_OUT, name="B_OUT")    # skipped dropout for exported graph as there is no need for already calculated weights    OUTPUT = tf.nn.softmax(tf.matmul(FC1, W_OUT) + B_OUT, name="output")    sess = tf.Session()    init = tf.initialize_all_variables()    sess.run(init)    graph_def = g.as_graph_def()    tf.train.write_graph(graph_def, EXPORT_DIR, 'mnist_model_graph.pb', as_text=False)    # Test trained model    y_train = tf.placeholder("float", [None, 10])    correct_prediction = tf.equal(tf.argmax(OUTPUT, 1), tf.argmax(y_train, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    print("check accuracy %g" % accuracy.eval({x_2: mnist.test.images, y_train: mnist.test.labels}, sess))

重要的

    graph_def = g.as_graph_def()tf.train.write_graph(graph_def, EXPORT_DIR, 'mnist_model_graph.pb', as_text=False)