上面2.6.1的例子很奇怪，试过np.array与直接 定义个列表好像也没什么影响~~，不一定要np.asarray~~情况如下：
[['a' 'a'] ['b' 'b']] [0 1] [['c' 'c'] ['d' 'd']] [2 3] [['e' 'e'] ['f' 'f']] [4 5] ~~~~~~~~~~~~~~~ [['e' 'e'] ['a' 'a']] [4 0] [['c' 'c'] ['b' 'b']] [2 1] [['f' 'f'] ['d' 'd']] [5 3] ~~~~~~~~~~~~~~~ [['a', 'a'], ['b', 'b'], ['c', 'c']] [0, 1, 2] [['d', 'd'], ['e', 'e'], ['f', 'f']] [3, 4, 5] [Finished in 4.3s] 下面是2.6.2的例子，还是很顺利的，就是要注意# tl.logging.set_verbosity(tl.logging.DEBUG)要注释掉，不然会报错，说是tensorlayer还没logging这个功能，另一个是network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output')这行的act要定义个算法，
https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py
import time import tensorflow as tf import tensorlayer as tl tf.logging.set_verbosity(tf.logging.DEBUG) # tl.logging.set_verbosity(tl.logging.DEBUG) sess = tf.InteractiveSession() # prepare data X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784)) # define placeholder x = tf.placeholder(tf.float32, shape=[None, 784], name='x') y_ = tf.placeholder(tf.int64, shape=[None], name='y_') # define the network network = tl.layers.InputLayer(x, name='input') network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1') network = tl.layers.DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu1') network = tl.layers.DropoutLayer(network, keep=0.5, name='drop2') network = tl.layers.DenseLayer(network, n_units=800, act=tf.nn.relu, name='relu2') network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3') # the softmax is implemented internally in tl.cost.cross_entropy(y, y_) to # speed up computation, so we use identity here. # see tf.nn.sparse_softmax_cross_entropy_with_logits() network = tl.layers.DenseLayer(network, n_units=10, act=tf.identity, name='output') # define cost function and metric. y = network.outputs cost = tl.cost.cross_entropy(y, y_, name='xentropy') correct_prediction = tf.equal(tf.argmax(y, 1), y_) acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) y_op = tf.argmax(tf.nn.softmax(y), 1) # define the optimizer train_params = network.all_params train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cost, var_list=train_params) # initialize all variables in the session tl.layers.initialize_global_variables(sess) # print network information network.print_params() network.print_layers() n_epoch = 100 batch_size = 500 print_freq = 10 for epoch in range(n_epoch): start_time = time.time() for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True): feed_dict = {x: X_train_a, y_: y_train_a} feed_dict.update(network.all_drop) # enable noise layers sess.run(train_op, feed_dict=feed_dict) if epoch + 1 == 1 or (epoch + 1) % print_freq == 0: print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time)) train_loss, train_acc, n_batch = 0, 0, 0 for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True): dp_dict = tl.utils.dict_to_one(network.all_drop) # disable noise layers feed_dict = {x: X_train_a, y_: y_train_a} feed_dict.update(dp_dict) err, ac = sess.run([cost, acc], feed_dict=feed_dict) train_loss += err train_acc += ac n_batch += 1 print(" train loss: %f" % (train_loss / n_batch)) print(" train acc: %f" % (train_acc / n_batch)) val_loss, val_acc, n_batch = 0, 0, 0 for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True): dp_dict = tl.utils.dict_to_one(network.all_drop) # disable noise layers feed_dict = {x: X_val_a, y_: y_val_a} feed_dict.update(dp_dict) err, ac = sess.run([cost, acc], feed_dict=feed_dict) val_loss += err val_acc += ac n_batch += 1 print(" val loss: %f" % (val_loss / n_batch)) print(" val acc: %f" % (val_acc / n_batch)) print('~~~~~~~~~~~~~Evaluation~~~~~~~~~~~~~') test_loss, test_acc, n_batch = 0, 0, 0 for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=True): dp_dict = tl.utils.dict_to_one(network.all_drop) # disable noise layers feed_dict = {x: X_test_a, y_: y_test_a} feed_dict.update(dp_dict) err, ac = sess.run([cost, acc], feed_dict=feed_dict) test_loss += err test_acc += ac n_batch += 1 print(" test loss: %f" % (test_loss / n_batch)) print(" test acc: %f" % (test_acc / n_batch)) 因为是在家里运行，电脑比单位的又更破了一个等级，所以n_epoch从500降成了100~~运行如下，共花了两千多秒~~~
[TL] Load or Download MNIST > data\mnist [TL] data\mnist\train-images-idx3-ubyte.gz [TL] data\mnist\t10k-images-idx3-ubyte.gz [TL] InputLayer input: (?, 784) [TL] DropoutLayer drop1: keep:0.800000 is_fix:False [TL] DenseLayer relu1: 800 relu [TL] DropoutLayer drop2: keep:0.500000 is_fix:False [TL] DenseLayer relu2: 800 relu [TL] DropoutLayer drop3: keep:0.500000 is_fix:False [TL] DenseLayer output: 10 identity [TL] param 0: relu1/W:0 (784, 800) float32_ref (mean: -3.3809192245826125e-05, median: 5.016334034735337e-05, std: 0.08794602751731873) [TL] param 1: relu1/b:0 (800,) float32_ref (mean: 0.0 , median: 0.0 , std: 0.0 ) [TL] param 2: relu2/W:0 (800, 800) float32_ref (mean: 6.852106162114069e-05, median: 3.730915341293439e-05, std: 0.08797246217727661) [TL] param 3: relu2/b:0 (800,) float32_ref (mean: 0.0 , median: 0.0 , std: 0.0 ) [TL] param 4: output/W:0 (800, 10) float32_ref (mean: 0.000250009645242244, median: -0.0002332517469767481, std: 0.0876460000872612) [TL] param 5: output/b:0 (10,) float32_ref (mean: 0.0 , median: 0.0 , std: 0.0 ) [TL] num of params: 1276810 [TL] layer 0: drop1/mul:0 (?, 784) float32 [TL] layer 1: relu1/Relu:0 (?, 800) float32 [TL] layer 2: drop2/mul:0 (?, 800) float32 [TL] layer 3: relu2/Relu:0 (?, 800) float32 [TL] layer 4: drop3/mul:0 (?, 800) float32 [TL] layer 5: output/Identity:0 (?, 10) float32 Epoch 1 of 100 took 26.056490s train loss: 0.592533 train acc: 0.809320 val loss: 0.531577 val acc: 0.830900 Epoch 10 of 100 took 25.144438s train loss: 0.244603 train acc: 0.928800 val loss: 0.223174 val acc: 0.938600 Epoch 20 of 100 took 25.214442s train loss: 0.171495 train acc: 0.952060 val loss: 0.164636 val acc: 0.955400 Epoch 30 of 100 took 25.943484s train loss: 0.131061 train acc: 0.963680 val loss: 0.133256 val acc: 0.963200 Epoch 40 of 100 took 24.997430s train loss: 0.102254 train acc: 0.972000 val loss: 0.111744 val acc: 0.967300 Epoch 50 of 100 took 24.961428s train loss: 0.083485 train acc: 0.977180 val loss: 0.098263 val acc: 0.972600 Epoch 60 of 100 took 24.688412s train loss: 0.068905 train acc: 0.981760 val loss: 0.088438 val acc: 0.974000 Epoch 70 of 100 took 25.372451s train loss: 0.057064 train acc: 0.984420 val loss: 0.080247 val acc: 0.975900 Epoch 80 of 100 took 25.180440s train loss: 0.047653 train acc: 0.987080 val loss: 0.074070 val acc: 0.977000 Epoch 90 of 100 took 24.923426s train loss: 0.040897 train acc: 0.989320 val loss: 0.070247 val acc: 0.979100 Epoch 100 of 100 took 25.322448s train loss: 0.034475 train acc: 0.991020 val loss: 0.066125 val acc: 0.980400 ~~~~~~~~~~~~~Evaluation~~~~~~~~~~~~~ test loss: 0.064027 test acc: 0.979400 [Finished in 2658.8s] 按教材是说，这节的设计是用来调参的，但是我还是一脸不懂~~以后明白了再回来添加吧，先placeholder个吧
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这个github.com是越来越慢了，今天顺便百度找了个办法加速了一下，在这个网址上http://tool.chinaz.com/dns?type=1&host=github.com&ip=
然后找到host文件，用文本txt打开，在最后一行加上IP 和github.com,然后重开浏览器速度就快多了，这里的ip是会变的，下次再变慢就再搜再加吧，windows7可以直接改host文件的,windows10有时好像是不让改的，这时得用管理员权限了，用管理员权限打开cmd,再通过cmd打开host文件来改，windows7的过程如下，win10的要自己百度了：
