本文主要是介绍MNIST3_tf2.keras训练预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
针对MNIST数据集进行j基于tf2.keras的nn模型训练和预测
部分脚本如下： 完整脚本见笔者github
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, Sequential, regularizers, Model
from utils.utils_tools import clock, get_ministdata
import warnings
warnings.filterwarnings(action='ignore')class MyModel(Model):def __init__(self):super(MyModel, self).__init__()self.fc0 = layers.Dense(256, activation='relu')self.bn1 = layers.BatchNormalization()self.fc1 = layers.Dense(32, activation='relu', kernel_regularizer = regularizers.l2(0.06))self.bn2 = layers.BatchNormalization()self.fc2 = layers.Dense(10, activation='sigmoid')def call(self, inputs_, training=None):x = self.fc0(inputs_)x = self.bn1(x)x = self.fc1(x)x = self.bn2(x)return self.fc2(x)def build_nnmodel(input_shape=(None, 784)):nnmodel = MyModel()nnmodel.build(input_shape=input_shape)nnmodel.compile(loss='categorical_crossentropy',optimizer = 'adam',metrics = ['accuracy'])return nnmodelif __name__ == '__main__':mnistdf = get_ministdata()te_index = mnistdf.sample(frac=0.75).index.tolist()mnist_te = mnistdf.loc[te_index, :]mnist_tr = mnistdf.loc[~mnistdf.index.isin(te_index), :]mnist_tr_x, mnist_tr_y = mnist_tr.iloc[:, :-1].values, tf.keras.utils.to_categorical(mnist_tr.iloc[:, -1].values)mnist_te_x, mnist_te_y = mnist_te.iloc[:, :-1].values, tf.keras.utils.to_categorical(mnist_te.iloc[:, -1].values)nnmodel = build_nnmodel()stop = tf.keras.callbacks.EarlyStopping(monitor = 'accuracy', min_delta=0.0001)history = nnmodel.fit(mnist_tr_x, mnist_tr_y,validation_data = (mnist_te_x, mnist_te_y),epochs=10,callbacks = [stop])acc_final = round(max(history.history['accuracy']), 2)print(f"acc:{acc_final:.3f}")predict_ = np.argmax(nnmodel.predict(mnist_te_x), axis=1)te_y = np.argmax(mnist_te_y, axis=1)print(predict_)print(te_y)print(f'auc: {sum(predict_ == te_y)/te_y.shape[0]:.3f}')
"""
14000/14000 [==============================] - 10s 741us/sample - loss: 0.1472 - accuracy: 0.9730 - val_loss: 0.2162 - val_accuracy: 0.9557
Epoch 9/10
13952/14000 [============================>.] - ETA: 0s - loss: 0.1520 - accuracy: 0.9720
Epoch 00009: accuracy did not improve from 0.97300
14000/14000 [==============================] - 23s 2ms/sample - loss: 0.1522 - accuracy: 0.9719 - val_loss: 0.2390 - val_accuracy: 0.9506
acc:0.970
[2 2 8 ... 7 6 6]
[2 2 8 ... 7 6 6]
auc: 0.951, cost: 170.946"""这篇关于MNIST3_tf2.keras训练预测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
