Tensorflow2 keras 分类模型

Tensorflow2 keras 分类模型

from pickletools import optimizefrom pyexpat import modelfrom re import Xfrom tkinter import Yimport matplotlib as mplimport matplotlib.pyplot as pltfrom sklearn.preprocessing import StandardScalerimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport timefrom sklearn import metricsimport tensorflow as tffrom tensorflow import keras#数据集fashion_mnist = keras.datasets.fashion_mnist#训练集和测试集(x_train_all,y_train_all),(x_test,y_test) = fashion_mnist.load_data()#验证集和训练集x_valid,x_train = x_train_all[:5000],x_train_all[5000:]y_valid,y_train = y_train_all[:5000],y_train_all[5000:]# 训练集归一化# x = （x - u）/ std ：x - 均值 / 方差 scaler = StandardScaler()x_train_scaled = scaler.fit_transform( x_train.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)x_valid_scaled = scaler.transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)x_test_scaled = scaler.transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)def show_single_image(img_arr): plt.imshow(img_arr,cmap="binary") plt.show()def show_imgs(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) #指定图像宽高 英尺单位 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 #创建单个子图 plt.subplot(n_rows,n_cols,index + 1) plt.imshow(x_data[index],cmap="binary",interpolation='nearest') plt.axis('off') plt.title(class_names[y_data[index]]) plt.show()class_names = ['T-shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']#show_imgs(3,5,x_train,y_train,class_names)#添加模型 sequential线性堆叠模型model = keras.models.Sequential()#将28*28的矩阵展平为一维向量model.add(keras.layers.Flatten(input_shape=[28,28]))#Dense：每一层的输入来自前面所有层的输出->解决梯度消失的问题#梯度消失和梯度爆炸：计算深度增加导致求导数据持续过低（0-0.25）或过高（1）model.add(keras.layers.Dense(300,activation="relu"))#此100单元与300单元做全联接#relu：y = max(0,x) 大于0返回x#softmax：将向量变成概率分布 x = [x1,x2,x3]# y = [e^x1/sum, e^x2/sum,e^x3/sum] sum = e^x1/sum+e^x2/sum+e^x3/summodel.add(keras.layers.Dense(100,activation="relu"))model.add(keras.layers.Dense(10,activation="softmax"))# sparse_categorical_crossentropy: y是一个数值需要将 y->one_hot->[] 转化为向量,如果是向量需要用categorical_crossentropy# optimize 模型调整方法# metrics# optimizer="adam" sgd ->梯度优化算法 model.compile(loss="sparse_categorical_crossentropy",optimizer="adam", metrics = ["accuracy"])#模型架构显示#架构参数：#1层 [None,784] [样本数*784]#2层 第一层转化为 [None,300] ：[none,784] * w + b -> [none,300] w.shape[784,300], b=[300]model.summary()#结果验证 history = model.fit(x_train_scaled,y_train,epochs=10,validation_data=(x_valid_scaled,y_valid))# history.history#结果估值print(model.evaluate(x_test_scaled,y_test))#结果可视化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)

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