java中的接口是类吗
232
2022-10-23
利用深度神经网络进行基本图像分类
利用深度神经网络进行基本图像分类
利用深度神经网络进行基本图像分类
此代码在colab已经经过测试并且可以查看TensorBoard：地址
环境：Tensorflow-2.1.0、Python3.6、google colab
In[1]:
import ssl ssl._create_default_https_context = ssl._create_unverified_context# 不知道为什么在本地的jupyter notebook上无法导入fashion_mnist数据集(本地需要，google colab不需要)
In[2]:
import matplotlib as mplimport matplotlib.pyplot as plt%matplotlib inlineimport numpy as npimport sklearn import pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import keras as kerasfrom sklearn.preprocessing import StandardScalerfor module in mpl,np,pd,sklearn,tf,keras:print(module.__name__,module.__version__)# 打印版本库
Out[1]:
matplotlib 3.1.2 numpy 1.18.1 pandas 0.25.3 sklearn 0.22.1 tensorflow 2.1.0 tensorflow_core.keras 2.2.4-tf
In[3]:
fashion_mnist=keras.datasets.fashion_mnist(x_train_all, y_train_all),(x_test, y_test)=fashion_mnist.load_data()# 导入fashion_mnist数据集
In[4]
x_valid,x_train=x_train_all[:5000],x_train_all[5000:]y_valid,y_train=y_train_all[:5000],y_train_all[5000:]# fashion_mnist有6w个数据，前5k个为验证集，其中x为图片，y为序列号print(x_valid.shape,y_valid.shape)print(x_train.shape,y_valid.shape)print(x_test.shape,y_test.shape)# 打印数据集的格式
Out[2]
(5000, 28, 28) (5000,) (55000, 28, 28) (5000,) (10000, 28, 28) (10000,)
In[5]:
print(np.max(x_train),np.min(x_train))# 打印数据集中的最大值和最小值
Out[3]:
255 0
In[6]:
# x= (x-u)/stdscaler=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) #会自动记录方差u和均值std，并进行归一化x_valid_scaled=scaler.transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28) # 会根据自动记录的方差u和均值std进行归一化x_test_scaled=scaler.transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28) # 会根据自动记录的方差u和均值std进行归一化
Out[4]:
2.0231433 -0.8105136
In[7]:
def show_single_image(img_arr):plt.imshow(img_arr,cmap='binary')plt.show()# 打印单张图片show_single_image(x_train[0])
Out[5]:
In[8]:
def show_images(n_rows,n_cols,x_data,y_data,class_names):assert len(x_data)==len(y_data)assert n_cols*n_rows Out[6]: In[9]: # 构建一个三层的神经网络# model=keras.models.Sequential()# model.add(keras.layers.Flatten(input_shape=[28,28])) # 展开成28*28的向量# model.add(keras.layers.Dense(300,activation='relu')) # 全链接层，层数300，激活函数relu# model.add(keras.layers.Dense(100,activation='relu')) # model.add(keras.layers.Dense(10,activation='softmax')) # 有10个输出# model=keras.models.Sequential([# keras.layers.Flatten(input_shape=[28,28]),# keras.layers.Dense(300,activation='relu'),# keras.layers.Dense(100,activation='relu'),# keras.layers.Dense(10,activation='softmax')# ])model=keras.models.Sequential()model.add(keras.layers.Flatten(input_shape=[28,28]))for _ in range(20):model.add(keras.layers.Dense(100,activation='selu'))# model.add(keras.layers.Dense(100,activation='relu'))# model.add(keras.layers.BatchNormalization())# 激活函数在前的后的批归一化# model.add(keras.layers.Dense(100))# model.add(keras.layers.BatchNormalization()) # model.add(keras.layers.Activation('relu'))# model.add(keras.layers.Dropout(rate=0.5))model.add(keras.layers.AlphaDropout(rate=0.5))# AlphaDropout与Dropout的区别：1、均值方差不变 2、归一化性质不变model.add(keras.layers.Dense(10,activation='softmax'))# relu: y=max(0,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+e^x2+e^x3# reason for sparse: y->index y->one_hot->[]model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) tensorflow.keras.model.compile In[10]: model.layers Out[7]: [ In[11]: model.summary() #打印模型的结构 Out[8]: Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 784) 0
_________________________________________________________________
dense (Dense) (None, 100) 78500
_________________________________________________________________
dense_1 (Dense) (None, 100) 10100
_________________________________________________________________
dense_2 (Dense) (None, 100) 10100
_________________________________________________________________
dense_3 (Dense) (None, 100) 10100
_________________________________________________________________
dense_4 (Dense) (None, 100) 10100
_________________________________________________________________
dense_5 (Dense) (None, 100) 10100
_________________________________________________________________
dense_6 (Dense) (None, 100) 10100
_________________________________________________________________
dense_7 (Dense) (None, 100) 10100
_________________________________________________________________
dense_8 (Dense) (None, 100) 10100
_________________________________________________________________
dense_9 (Dense) (None, 100) 10100
_________________________________________________________________
dense_10 (Dense) (None, 100) 10100
_________________________________________________________________
dense_11 (Dense) (None, 100) 10100
_________________________________________________________________
dense_12 (Dense) (None, 100) 10100
_________________________________________________________________
dense_13 (Dense) (None, 100) 10100
_________________________________________________________________
dense_14 (Dense) (None, 100) 10100
_________________________________________________________________
dense_15 (Dense) (None, 100) 10100
_________________________________________________________________
dense_16 (Dense) (None, 100) 10100
_________________________________________________________________
dense_17 (Dense) (None, 100) 10100
_________________________________________________________________
dense_18 (Dense) (None, 100) 10100
_________________________________________________________________
dense_19 (Dense) (None, 100) 10100
_________________________________________________________________
alpha_dropout (AlphaDropout) (None, 100) 0
_________________________________________________________________
dense_20 (Dense) (None, 10) 1010
=================================================================
Total params: 271,410
Trainable params: 271,410
Non-trainable params: 0
_________________________________________________________________ In[12]: # 第一层 [None, 784] * w + b -> [None, 300] w.shape=[784,300], b=[300] In[13]: # TensorBoard,EarlyStopping,ModelCheckPointlogdir='./dnn-selu-dropout-callbacks' # 设置一个文件夹路径if not os.path.exists(logdir): # 若文件夹不存在，则创建文件夹os.mkdir(logdir)output_model_file=os.path.join(logdir, 'fashion_mnist_model.h5') #放入文件夹内，文件名fashion_mnist_model.h5callbacks=[keras.callbacks.TensorBoard(logdir),keras.callbacks.ModelCheckpoint(output_model_file,save_best_only=True), # 保存最好的模型，如果不是这个会默认保存最近的一个模型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) tensorflow.keras.model.fit Out[9]: Train on 55000 samples, validate on 5000 samples
Epoch 1/10
55000/55000 [==============================] - 11s 195us/sample - loss: 0.6998 - accuracy: 0.7617 - val_loss: 0.5656 - val_accuracy: 0.8536
Epoch 2/10
55000/55000 [==============================] - 8s 144us/sample - loss: 0.4626 - accuracy: 0.8428 - val_loss: 0.6195 - val_accuracy: 0.8558
Epoch 3/10
55000/55000 [==============================] - 9s 162us/sample - loss: 0.4065 - accuracy: 0.8581 - val_loss: 0.5300 - val_accuracy: 0.8668
Epoch 4/10
55000/55000 [==============================] - 8s 142us/sample - loss: 0.3755 - accuracy: 0.8717 - val_loss: 0.6154 - val_accuracy: 0.8664
Epoch 5/10
55000/55000 [==============================] - 8s 152us/sample - loss: 0.3521 - accuracy: 0.8774 - val_loss: 0.5287 - val_accuracy: 0.8766
Epoch 6/10
55000/55000 [==============================] - 8s 153us/sample - loss: 0.3343 - accuracy: 0.8815 - val_loss: 0.4956 - val_accuracy: 0.8856
Epoch 7/10
55000/55000 [==============================] - 8s 143us/sample - loss: 0.3193 - accuracy: 0.8857 - val_loss: 0.5349 - val_accuracy: 0.8716
Epoch 8/10
55000/55000 [==============================] - 8s 144us/sample - loss: 0.3085 - accuracy: 0.8916 - val_loss: 0.5294 - val_accuracy: 0.8842
Epoch 9/10
55000/55000 [==============================] - 8s 145us/sample - loss: 0.2955 - accuracy: 0.8946 - val_loss: 0.4668 - val_accuracy: 0.8822
Epoch 10/10
55000/55000 [==============================] - 8s 152us/sample - loss: 0.2839 - accuracy: 0.8975 - val_loss: 0.4907 - val_accuracy: 0.8860 In[14]: type(history) Out[10]: tensorflow.python.keras.callbacks.History In[15]: history.history# 打印每次学习的结果 Out[11]: {'loss': [0.6997782779043371,
0.4625766654621471,
0.4064591264659708,
0.37553091496554286,
0.35207060631621967,
0.3343261710730466,
0.3193407494176518,
0.30854321794509887,
0.29552189791636035,
0.28390346760099583],
'accuracy': [0.76165456,
0.84276366,
0.85809094,
0.8717273,
0.87743634,
0.8814909,
0.8856909,
0.89156365,
0.89461815,
0.8974909],
'val_loss': [0.5656432386219501,
0.6194551657661795,
0.5299783393919468,
0.6153697894215584,
0.5287225459891837,
0.49561948866695166,
0.5348633618405322,
0.5294362505242228,
0.4668049536377192,
0.4907300000913441],
'val_accuracy': [0.8536,
0.8558,
0.8668,
0.8664,
0.8766,
0.8856,
0.8716,
0.8842,
0.8822,
0.886]} In[16] def plot_learning_curves(history):pd.DataFrame(history.history).plot(figsize=(8,5))plt.grid(True)plt.gca().set_ylim(0,1)plt.show()# 打印学习曲线plot_learning_curves(history) Out[12]: In[17]: model.evaluate(x_test_scaled,y_test) # 在测试集上验证 tensorflow.keras.model.evaluate Out[13]: 10000/10000 [==============================] - 1s 95us/sample - loss: 0.5799 - accuracy: 0.8741 Out[14]: [0.5799049034297467, 0.8741] 汇总： #!/usr/bin/env python# coding: utf-8# In[1]:import ssl
ssl._create_default_https_context = ssl._create_unverified_context# 不知道为什么在本地的jupyter notebook上无法导入fashion_mnist数据集(本地需要，google colab不需要)# In[23]:import matplotlib as mplimport matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')import numpy as npimport sklearn
import pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import keras as kerasfrom sklearn.preprocessing import StandardScalerfor module in mpl,np,pd,sklearn,tf,keras:print(module.__name__,module.__version__)# 打印版本库# In[3]:fashion_mnist=keras.datasets.fashion_mnist(x_train_all, y_train_all),(x_test, y_test)=fashion_mnist.load_data()# 导入fashion_mnist数据集# In[4]:x_valid,x_train=x_train_all[:5000],x_train_all[5000:]y_valid,y_train=y_train_all[:5000],y_train_all[5000:]# fashion_mnist有6w个数据，前5k个为验证集，其中x为图片，y为序列号print(x_valid.shape,y_valid.shape)print(x_train.shape,y_valid.shape)print(x_test.shape,y_test.shape)# 打印数据集的格式# In[5]:print(np.max(x_train),np.min(x_train))# 打印数据集中的最大值和最小值# In[6]:# x= (x-u)/stdscaler=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) #会自动记录方差u和均值std，并进行归一化x_valid_scaled=scaler.transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28) # 会根据自动记录的方差u和均值std进行归一化x_test_scaled=scaler.transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28) # 会根据自动记录的方差u和均值std进行归一化# In[7]:def show_single_image(img_arr):plt.imshow(img_arr,cmap='binary')plt.show()# 打印单张图片show_single_image(x_train[0])# In[8]:def show_images(n_rows,n_cols,x_data,y_data,class_names):assert len(x_data)==len(y_data)assert n_cols*n_rows
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