基於tensorflow2對於fashion_mnist進行分類

import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import kerasimport tensorflow.compat.v1 as tftf.disable_v2_behavior()print(tf.__version__)print(sys.version_info)for module in mpl,np,pd,sklearn,tf,keras:    print(module.__name__,module.__version__)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)def show_single_image(img_arr):    plt.imshow(img_arr,cmap="binary")    plt.show()show_single_image(x_train[0])def show_imags(n_rows,n_cols,x_data,y_data,class_names):   assert len(x_data)==len(y_data)   assert n_rows*n_cols < len(x_data)   plt.figure(figsize=(n_cols*1.4,n_rows*1.6))   for row in range(n_rows):       for col in range(n_cols):           index = n_cols*row+col           plt.subplot(n_rows,n_cols,index+1)           plt.imshow(x_data[index],cmap = "binary",interpolation="nearest")           plt .axis("off")#不顯示座標尺寸           plt.title(class_names[y_data[index]])   plt.show()class_names = ['T-shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt',               'Sneaker','Bag','Ankle boot']show_imags(3,5,x_train,y_train,class_names)model = keras.models.Sequential()model.add(keras.layers.Flatten(input_shape = [28,28]))model.add(keras.layers.Dense(300,activation = 'relu'))model.add(keras.layers.Dense(100,activation = 'relu'))model.add(keras.layers.Dense(10,activation = 'softmax'))model.compile(loss = 'sparse_categorical_crossentropy',optimizer = 'sgd',              metrics = ['accuracy'])model.summmary()history = model.fit(x_train,y_train,epochs=10,validation_data=(x_valid,y_valid))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)