我是靠谱客的博主 爱听歌鞋子,这篇文章主要介绍带你构建最基础的卷积神经网络(CNN),现在分享给大家,希望可以做个参考。

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)

最后

以上就是爱听歌鞋子最近收集整理的关于带你构建最基础的卷积神经网络(CNN)的全部内容,更多相关带你构建最基础内容请搜索靠谱客的其他文章。

本图文内容来源于网友提供,作为学习参考使用,或来自网络收集整理,版权属于原作者所有。
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