import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import logging
import warnings
# 使用warnings模块来忽略特定类型的警告
warnings.simplefilter(action='ignore', category=FutureWarning)
# 配置tensorflow的日志记录级别
logging.getLogger("tensorflow").setLevel(logging.ERROR)
# 设置TensorFlow的autograph模块的详细级别
tf.autograph.set_verbosity(0)
# 设置numpy的打印选项
np.set_printoptions(precision=2)
# load dataset
def load_data():
X = np.load("Handwritten_Digit_Recognition_Multiclass_data/X.npy")
y = np.load("Handwritten_Digit_Recognition_Multiclass_data/y.npy")
return X, y
# load dataset
X, y = load_data()
print ('The shape of X is: ' + str(X.shape))
print ('The shape of y is: ' + str(y.shape))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
# # 绘图可选
# m, n = X.shape
# fig, axes = plt.subplots(8, 8, figsize=(5, 5))
# fig.tight_layout(pad=0.13, rect=[0, 0.03, 1, 0.91]) # [left, bottom, right, top]
# # fig.tight_layout(pad=0.5)
# for i, ax in enumerate(axes.flat):
# # Select random indices
# random_index = np.random.randint(m)
# # Select rows corresponding to the random indices and
# # reshape the image
# X_random_reshaped = X[random_index].reshape((20, 20)).T
# # Display the image
# ax.imshow(X_random_reshaped, cmap='gray')
# # Display the label above the image
# ax.set_title(y[random_index, 0])
# ax.set_axis_off()
# fig.suptitle("Label, image", fontsize=14)
# plt.show()
# 构建模型
tf.random.set_seed(1234) # 设置随机种子以确保每次运行的结果是一致的
model = Sequential(
[
### START CODE HERE ###
tf.keras.Input(shape=(400,)), # 输入层，输入数据的形状是400维
Dense(100, activation='relu', name="L1"), # 全连接层，100个神经元，使用ReLU激活函数，命名为"L1"
Dense(75, activation='relu', name="L2"), # 全连接层，75个神经元，使用ReLU激活函数，命名为"L2"
Dense(10, activation='linear', name="L3"), # 输出层，10个神经元，使用线性激活函数，命名为"L3"
### END CODE HERE ###
], name="my_model"
) # 定义模型名称为"my_model"
model.summary() # 打印模型的概述信息
# 配置模型的训练参数
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# 使用稀疏分类交叉熵作为损失函数，且输出是logits（即未经过softmax的原始输出）
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), # 使用Adam优化器，并设置学习率为0.001
)
# 训练模型
history = model.fit(
X_train, y_train, # 使用X_train作为输入数据，y_train作为目标数据
epochs=100 # 训练100轮
)
fig, axes = plt.subplots(20, 25, figsize=(20, 25))
fig.tight_layout(pad=0.13, rect=[0, 0.03, 1, 0.91]) # [left, bottom, right, top]
for i, ax in enumerate(axes.flat):
# Select random indices
random_index = np.random.randint(X_test.shape[0])
# Select rows corresponding to the random indices and
# reshape the image
X_random_reshaped = X_test[random_index].reshape((20, 20)).T
# Display the image
ax.imshow(X_random_reshaped, cmap='gray')
# Predict using the Neural Network
prediction = model.predict(X_test[random_index].reshape(1, 400))
prediction_p = tf.nn.softmax(prediction)
yhat = np.argmax(prediction_p)
# Display the label above the image
if y_test[random_index, 0] == yhat:
ax.set_title(f"{y_test[random_index, 0]},{yhat}", fontsize=10)
ax.set_axis_off()
else:
ax.set_title(f"{y_test[random_index, 0]},{yhat}", fontsize=10, color='red')
ax.set_axis_off()
fig.suptitle("Label, yhat", fontsize=14)
plt.show()
# 给出预测的测试集误差
def evaluation(y_test, y_predict):
accuracy=classification_report(y_test, y_predict,output_dict=True)['accuracy']
s=classification_report(y_test, y_predict,output_dict=True)['weighted avg']
precision=s['precision']
recall=s['recall']
f1_score=s['f1-score']
#kappa=cohen_kappa_score(y_test, y_predict)
return accuracy,precision,recall,f1_score #, kappa
y_pred=model.predict(X_test)
prediction_p = tf.nn.softmax(y_pred)
yhat = np.argmax(prediction_p, axis=1)
accuracy,precision,recall,f1_score=evaluation(y_test,yhat)
print("测试数据集准确率为：", accuracy)
print("测试数据集精确率为：", precision)
print("测试数据集召回率为：", recall)
print("测试数据集F1_score为：", f1_score)
