两个卷积神经的tensorboard图表对比

# encoding=utf-8import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data/', one_hot=True)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)myGraph = tf.Graph()with myGraph.as_default():        with tf.name_scope('inputs'):        x_raw = tf.placeholder(tf.float32, shape=[None, 784])        y = tf.placeholder(tf.float32, shape=[None, 10])    with tf.name_scope('conv-layer1'):        x = tf.reshape(x_raw, shape=[-1,28,28,1])        W_conv1 = weight_variable([5,5,1,32])        b_conv1 = bias_variable([32])        l_conv1 = tf.nn.relu(tf.nn.conv2d(x,W_conv1, strides=[1,1,1,1],padding='SAME') + b_conv1)        l_pool1 = tf.nn.max_pool(l_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')        tf.summary.image('x_input', x, max_outputs=10)        tf.summary.histogram('W_conv1', W_conv1)        tf.summary.histogram('b_conv1', b_conv1)    with tf.name_scope('conv-layer2'):        W_conv2 = weight_variable([5,5,32,64])        b_conv2 = bias_variable([64])        l_conv2 = tf.nn.relu(tf.nn.conv2d(l_pool1, W_conv2, strides=[1,1,1,1], padding='SAME')+b_conv2)        l_pool2 = tf.nn.max_pool(l_conv2, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')        tf.summary.histogram('W_conv2', W_conv2)        tf.summary.histogram('b_conv2', b_conv2)    with tf.name_scope('fc1'):        W_fc1 = weight_variable([64*7*7, 1024])        b_fc1 = bias_variable([1024])        l_pool2_flat = tf.reshape(l_pool2, [-1, 64*7*7])        l_fc1 = tf.nn.relu(tf.matmul(l_pool2_flat, W_fc1) + b_fc1)        keep_prob = tf.placeholder(tf.float32)        l_fc1_drop = tf.nn.dropout(l_fc1, keep_prob)        tf.summary.histogram('W_fc1', W_fc1)        tf.summary.histogram('b_fc1', b_fc1)    with tf.name_scope('fc2'):        W_fc2 = weight_variable([1024, 10])        b_fc2 = bias_variable([10])        y_conv = tf.matmul(l_fc1_drop, W_fc2) + b_fc2        tf.summary.histogram('W_fc1', W_fc1)        tf.summary.histogram('b_fc1', b_fc1)    with tf.name_scope('train'):        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))        train_step = tf.train.AdamOptimizer(learning_rate=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, tf.float32))        tf.summary.scalar('loss', cross_entropy)        tf.summary.scalar('accuracy', accuracy)with tf.Session(graph=myGraph) as sess:    sess.run(tf.global_variables_initializer())    merged = tf.summary.merge_all()    summary_writer = tf.summary.FileWriter('mnist-tensorboard/', graph=sess.graph)    for i in range(1001):        batch = mnist.train.next_batch(50)        sess.run(train_step, feed_dict={x_raw:batch[0], y:batch[1], keep_prob:0.7})        if i%100 == 0:            result, acc = sess.run([merged, accuracy], feed_dict={x_raw:batch[0], y:batch[1], keep_prob: 1.})            print("Iter " + str(i) + ", Training Accuracy= " + "{:.5f}".format(acc))            summary_writer.add_summary(result, i)        print("Optimization Finished!")    test_accuracy = sess.run(accuracy, feed_dict={x_raw: mnist.test.images[:256],                                                  y: mnist.test.labels[:256], keep_prob: 1.})    print("Testing Accuracy:", test_accuracy)

from __future__ import print_functionimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)learning_rate = 0.001training_iters = 200000batch_size = 128display_step = 10n_input = 784 n_classes = 10dropout = 0.75 with tf.name_scope('inputs'):    x = tf.placeholder(tf.float32, [None, n_input])    y = tf.placeholder(tf.float32, [None, n_classes])    keep_prob = tf.placeholder(tf.float32) def myconv2d(x, W, b, strides=1):    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):    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')def conv_net(x, weights, biases, dropout):    # 输入-1代表图片数量不定 , 28*28图片，1通道灰色图    with tf.name_scope('x'):        x = tf.reshape(x, shape=[-1, 28, 28, 1])    ############# 第一层卷积 #####################    with tf.name_scope('conv1'):        conv1 = myconv2d(x, weights['wc1'], biases['bc1'])        conv1 = maxpool2d(conv1, k=2)    ############# 第二层卷积 #####################    with tf.name_scope('conv2'):        conv2 = myconv2d(conv1, weights['wc2'], biases['bc2'])        conv2 = maxpool2d(conv2, k=2)    ############# 第三层全连接 ###################    with tf.name_scope('fc1'):        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)        fc1 = tf.nn.dropout(fc1, dropout)    ############# 第四层全连接 ###################    with tf.name_scope('fc2'):        out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])        return outweights = {    # 5x5 conv, 1 input, 32 outputs, 第一层卷积结果14*14*32    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),    # 5x5 conv, 32 inputs, 64 outputs, 第二层卷积结果7*7*64    '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 = {    # xx通道输出，对应xx偏置量    '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]))}predict = conv_net(x, weights, biases, keep_prob)############# 逻辑回归分类 #############with tf.name_scope('cost'):    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=y))    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)    tf.summary.scalar('cost', cost)############# 预测 #####################with tf.name_scope('train'):    correct_pred = tf.equal(tf.argmax(predict, 1), tf.argmax(y, 1))    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    tf.summary.scalar('accuracy', accuracy)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    step = 1    merged =tf.summary.merge_all()    writer = tf.summary.FileWriter('tmp-logs/', sess.graph)    ############# 训练 #####################    while step * batch_size < training_iters:        batch_x, batch_y = mnist.train.next_batch(batch_size)        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})        if step % display_step == 0:            result, loss, acc = sess.run([merged, 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))            writer.add_summary(result, step)        step += 1    print("Optimization Finished!")    test_accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images[:256],                                                  y: mnist.test.labels[:256], keep_prob: 1.})    print("Testing Accuracy:", test_accuracy)