[Tensorflow2.X][转载]深度可卷积神经网络实现mnist识别

from tensorflow import keras import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import pandas as pd import sklearn import os import sys   def plot_learning_curves(history):       pd.DataFrame(history.history).plot(figsize=(8,5))     plt.grid(True)     plt.gca().set_ylim(0,1)     plt.show()     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:]   #数据归一化 from sklearn.preprocessing import StandardScaler   scaler = StandardScaler() #x_train: [None, 28, 28] -> [None,784] x_train_scaled = scaler.fit_transform(x_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28,1) x_valid_scaled = scaler.transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28,1) x_test_scaled = scaler.transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28,1)       #使用relu函数 # model = tf.keras.models.Sequential() # model.add(keras.layers.Conv2D(filter=32,kernel_size=3, #                               padding='same',activation='relu', #                               input_shape=(28,28,1))) # # model.add(keras.layers.Conv2D(filters=32,kernel_size=3, #                               padding='same',activation='relu')) # model.add(keras.layers.MaxPooling2D(pool_size=2)) # # # model.add(keras.layers.Conv2D(filter=64, kernel_size=3, #                               padding='same',activation='relu')) # # model.add(keras.layers.Conv2D(filters=64,kernel_size=3, #                               padding='same',activation='relu')) # model.add(keras.layers.MaxPooling2D(pool_size=2)) # # model.add(keras.layers.Conv2D(filter=128,kernel_size=3, #                               padding='same',activation='relu')) # # model.add(keras.layers.Conv2D(filters=128,kernel_size=3, #                               padding='same',activation='relu')) # model.add(keras.layers.MaxPooling2D(pool_size=2)) # model.add(keras.layers.Flatten()) # # model.add(keras.layers.Dense(128,activation='relu')) # model.add(keras.layers.Dense(10,activation='softmax'))   #使用selu函数 model = tf.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.SeparableConv2D(filters=32,kernel_size=3,                                         padding='same',activation='selu')) model.add(keras.layers.MaxPooling2D(pool_size=2))     model.add(keras.layers.SeparableConv2D(filters=64, kernel_size=3,                               padding='same',activation='selu'))   model.add(keras.layers.SeparableConv2D(filters=64,kernel_size=3,                               padding='same',activation='selu')) model.add(keras.layers.MaxPooling2D(pool_size=2))   model.add(keras.layers.SeparableConv2D(filters=128,kernel_size=3,                               padding='same',activation='selu'))   model.add(keras.layers.SeparableConv2D(filters=128,kernel_size=3,                               padding='same',activation='selu')) model.add(keras.layers.MaxPooling2D(pool_size=2)) model.add(keras.layers.Flatten())   model.add(keras.layers.Dense(128,activation='selu')) #model.add(keras.layers.Dense(10,activation='softmax')) model.compile(loss='sparse_categorical_crossentropy', optimizer='', metrics=['accuracy'])   #使用了三个callback：Tensorboard, earlystopping, ModelCheckpoint #logdir = './spearable-cnn-selu-callbacks' logdir = os.path.join("dnn-callbacks") if not os.path.exists(logdir):     os.mkdir(logdir) output_model_file = os.path.join(logdir,"fashion_mnist_model.h5") print("out:",output_model_file) callbacks = [     tf.keras.callbacks.TensorBoard(logdir),     tf.keras.callbacks.ModelCheckpoint(output_model_file,save_best_only=True),     tf.keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3) ] history = model.fit(x_train_scaled, y_train, epochs=10, validation_data=(x_valid_scaled, y_valid),callbacks=callbacks)   plot_learning_curves(history)