带你构建最基础的卷积神经网络(CNN)

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras



'''查看版本号'''

print(tf.__version__)
print(sys.version_info)
for module in mpl,np,pd,sklearn,tf,keras:
    print(module.__name__,module.__version__)

'''load the data'''

fashion_mnist = keras.datasets.fashion_mnist
(x_train_all,y_train_all),(x_test,y_test)=fashion_mnist.load_data()
x_valid,x_train=x_train_all[:5000],x_train_all[5000:]
y_valid,y_train=y_train_all[:5000],y_train_all[5000:]

print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)


'''数据处理'''
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train_scaled=scaler.fit_transform(
    x_train.astype(np.float32).reshape(-1,1)).reshape(-1,28,28,1)
x_valid_scaled=scaler.fit_transform(
    x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28,1)
x_test_scaled=scaler.fit_transform(
    x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28,1)


'''模型构建'''
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32,kernel_size=3,
                             padding='same',
                             activation='selu',
                             input_shape=(28,28,1)))
model.add(keras.layers.Conv2D(filters=32,kernel_size=3,
                             padding='same',
                             activation='selu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Conv2D(filters=64,kernel_size=3,
                             padding='same',
                             activation='selu'))
model.add(keras.layers.Conv2D(filters=64,kernel_size=3,
                             padding='same',
                             activation='selu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Conv2D(filters=128,kernel_size=3,
                             padding='same',
                             activation='selu'))
model.add(keras.layers.Conv2D(filters=128,kernel_size=3,
                             padding='same',
                             activation='selu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128,activation='relu'))
model.add(keras.layers.Dense(10,activation="softmax"))



model.compile(loss="sparse_categorical_crossentropy",
             optimizer = "sgd",
             metrics = ["accuracy"])
logdir = './cnn-selu-callbacks'
if not os.path.exists(logdir):
    os.mkdir(logdir)
output_model_file = os.path.join(logdir,
                                "fashion mnist model.h5")

callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.ModelCheckpoint(output_model_file,
                                   save_best_only = True),
    keras.callbacks.EarlyStopping(patience=5,min_delta=1e-3),

]

'''训练'''
history = model.fit(x_train_scaled,y_train,epochs=10,
                   validation_data=(x_valid_scaled,y_valid),
                   callbacks = callbacks)

'''画图看趋势'''
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8,5))
    plt.grid(True)
    plt.gca().set_ylim(0,1)
    plt.show()

plot_learning_curves(history)
model.evaluate(x_test_scaled,y_test)

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