《Tensorflow + Keras深度学习人工智能实践应用》 一书第8章的完整例子，相对于第7章的感知机，第八章使用了神经网络，进一步提高了准确率。
在实现上，主要增加了卷积层和池化层。卷积层，使用了filters（即使用多少个卷积核）和卷积核的大小，以及做卷积的填充方式。池化，主要缩减了图片的大小。
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 Dropout, Flatten, Conv2D, MaxPooling2Dimport 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(x_train_image.shape[0], 28, 28, 1).astype("float32")
x_test = x_test_image.reshape(x_test_image.shape[0], 28, 28, 1).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(Conv2D(filters=16,
kernel_size = (5, 5),
padding = 'same',
input_shape = (28, 28, 1),
activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=36,
kernel_size = (5, 5),
padding = 'same',
activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(units = 128,
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 = 300,
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 - 43s - loss: 0.5479 - acc: 0.8286 - val_loss: 0.1138 - val_acc: 0.9670 Epoch 2/10 - 44s - loss: 0.1516 - acc: 0.9541 - val_loss: 0.0766 - val_acc: 0.9765 Epoch 3/10 - 46s - loss: 0.1127 - acc: 0.9663 - val_loss: 0.0575 - val_acc: 0.9827 Epoch 4/10 - 46s - loss: 0.0937 - acc: 0.9721 - val_loss: 0.0502 - val_acc: 0.9857 Epoch 5/10 - 46s - loss: 0.0812 - acc: 0.9756 - val_loss: 0.0457 - val_acc: 0.9870 Epoch 6/10 - 45s - loss: 0.0693 - acc: 0.9789 - val_loss: 0.0426 - val_acc: 0.9879 Epoch 7/10 - 46s - loss: 0.0617 - acc: 0.9808 - val_loss: 0.0410 - val_acc: 0.9883 Epoch 8/10 - 44s - loss: 0.0568 - acc: 0.9830 - val_loss: 0.0375 - val_acc: 0.9896 Epoch 9/10 - 44s - loss: 0.0523 - acc: 0.9845 - val_loss: 0.0358 - val_acc: 0.9898 Epoch 10/10 - 43s - loss: 0.0468 - acc: 0.9859 - val_loss: 0.0360 - val_acc: 0.9895 10000/10000 [==============================] - 4s 389us/step accuracy = 0.9922
作者：YANWeichuan
链接：https://www.jianshu.com/p/47e601c6c2e1
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