《Tensorflow + Keras深度学习人工智能实践应用》 一书第7章的完整例子，一行行代码敲出来，更有利于理解整个例子的工作流程和结果。
机器学习的几个分类：
多层感知器（Multi-layer Perceptron，MLP）
深度神经网络（Deep Neural Network，DNN）
卷积神经网络（Convolutional Neural Network，CNN）
递归神经网络（Recurrent Neural Network，RNN）
import numpy as npimport pandas as pdfrom keras.utils import np_utilsfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers import Dropoutimport matplotlib.pyplot as pltimport os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'np.random.seed(10)
(x_train_image, y_train_label), (x_test_image, y_test_label) = mnist.load_data()
x_train = x_train_image.reshape(60000, 784).astype("float32")
x_test = x_test_image.reshape(10000, 784).astype("float32")
x_train_normal = x_train / 255x_test_normal = x_test / 255y_train_onehot = np_utils.to_categorical(y_train_label)
y_test_onehot = np_utils.to_categorical(y_test_label)
model = Sequential()
model.add(Dense(units = 1000,
input_dim = 784,
kernel_initializer = 'normal',
activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 1000,
kernel_initializer = 'normal',
activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 10,
kernel_initializer = 'normal',
activation = 'softmax'))
print(model.summary())
model.compile(loss = "categorical_crossentropy",
optimizer = "adam", metrics = ["accuracy"])
history = model.fit(x = x_train_normal,
y = y_train_onehot,
validation_split = 0.2,
epochs = 10,
batch_size = 200,
verbose = 2)def show_train_history(train_history, train, val):
plt.plot(train_history.history[train])
plt.plot(train_history.history[val])
plt.title("Train History")
plt.ylabel(train)
plt.xlabel("Epochs")
plt.legend(["train", "validation"], loc="upper left")
plt.show()def plot_image_label_prediction(images, labels, prediction, idx = 0, num = 10):
fig = plt.gcf()
fig.set_size_inches(12, 14) if num > 25:
num = 25
for i in range(0, num):
ax = plt.subplot(5, 5, 1 + i)
ax.imshow(images[idx], cmap="binary")
title = "label = " + str(labels[idx]) if len(prediction) > 0:
title += ", prediction = " + str(prediction[idx])
ax.set_title(title, fontsize = 12)
ax.set_xticks([])
ax.set_yticks([])
idx += 1
plt.show()
show_train_history(history, "acc", "val_acc")
show_train_history(history, "loss", "val_loss")
scores = model.evaluate(x_test_normal, y_test_onehot)
print("accuracy = ", scores[1])
prediction = model.predict_classes(x_test_normal)#plot_image_label_prediction(x_test_image, y_test_label, prediction, idx=340, num=25)print(pd.crosstab(y_test_label, prediction, rownames = ["label"], colnames = ["predict"]))
df = pd.DataFrame({"label": y_test_label, "predict": prediction})
print(df[(df.label == 5) & (df.predict == 3)])训练及精度：
Train on 48000 samples, validate on 12000 samples Epoch 1/10 - 10s - loss: 0.3634 - acc: 0.8870 - val_loss: 0.1342 - val_acc: 0.9608 Epoch 2/10 - 10s - loss: 0.1585 - acc: 0.9520 - val_loss: 0.1003 - val_acc: 0.9702 Epoch 3/10 - 10s - loss: 0.1182 - acc: 0.9626 - val_loss: 0.0889 - val_acc: 0.9725 Epoch 4/10 - 10s - loss: 0.0965 - acc: 0.9704 - val_loss: 0.0857 - val_acc: 0.9745 Epoch 5/10 - 10s - loss: 0.0838 - acc: 0.9733 - val_loss: 0.0798 - val_acc: 0.9781 Epoch 6/10 - 10s - loss: 0.0762 - acc: 0.9756 - val_loss: 0.0803 - val_acc: 0.9770 Epoch 7/10 - 10s - loss: 0.0640 - acc: 0.9800 - val_loss: 0.0756 - val_acc: 0.9770 Epoch 8/10 - 10s - loss: 0.0625 - acc: 0.9798 - val_loss: 0.0787 - val_acc: 0.9765 Epoch 9/10 - 10s - loss: 0.0550 - acc: 0.9816 - val_loss: 0.0751 - val_acc: 0.9802 Epoch 10/10 - 10s - loss: 0.0520 - acc: 0.9836 - val_loss: 0.0780 - val_acc: 0.9772 10000/10000 [==============================] - 1s 100us/step accuracy = 0.9797
作者：YANWeichuan
链接：https://www.jianshu.com/p/7f134588cdcc