四次作业
BP神经网络
import numpy as np import matplotlib.pyplot as plt #避免中文乱码 plt.rcParams['font.sans-serif'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False #定义激活函数 def sigmoid(x): return 1/(1 + np.exp(-x)) #BP神经网络 def bpnet(x = None,y = None,hidsize = 5,maxiter = None,yita = 0.05): n,m = x.shape net_in = -np.ones([m+1]) out_in = -np.ones([hidsize + 1]) w_mid = np.random.rand(m+1,hidsize) #初始隐层神经元的权值 w_out = np.random.rand(hidsize+1) #初始输出神经元的权值 delta_w_mid = np.zeros([m+1,hidsize]) #初始中间层权值的修正量 Error = np.zeros([maxiter]) for it in range(maxiter): error = np.zeros([n]) for j in range(n): net_in[:m] = x[j,:m] #更新网络输入值 real = y[j] #对应实际值 for i in range(hidsize): out_in[i] = sigmoid(sum(net_in*w_mid[:,i])) res = sigmoid(sum(out_in*w_out))#隐藏层到网络输出 #输出层权值修正 delta_w_out = yita*res*(1-res)*(real-res)*out_in delta_w_out[hidsize] = -yita*res*(1-res)*(real-res) w_out = w_out + delta_w_out #中间层权值修正量 for i in range(hidsize): delta_w_mid[:,i] = yita*out_in[i]*(1-out_in[i])*w_out[i]*res*(1-res)*(real-res)*net_in delta_w_mid[m,i] = -yita*out_in[i]*(1-out_in[i])*w_out[i]*res*(1-res)*(real-res) w_mid = w_mid + delta_w_mid error[j] = abs(real-res) Error[it] = error.mean() print('第%s次迭代，误差是%s' % (it,Error[it])) plt.plot(Error) plt.xlabel('迭代次数') plt.ylabel('误差') plt.title('模型训练误差') plt.show() return w_mid,w_out #####################测试 from sklearn.datasets import load_iris dataset = load_iris() data = dataset['data'] target = dataset['target'] w_mid,w_out = bpnet(x = data,y = target,maxiter = 10)
TensorFlow
# TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot as plt print(tf.__version__) fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] plt.figure() plt.imshow(train_images[0]) plt.colorbar() plt.grid(False) plt.show() train_images = train_images / 255.0 test_images = test_images / 255.0 plt.figure(figsize=(10,10)) for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]]) plt.show() model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) model.fit(train_images, train_labels, epochs=10) test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) print('\nTest accuracy:', test_acc) probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()]) predictions = probability_model.predict(test_images) def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array, true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([]) plt.imshow(img, cmap=plt.cm.binary) predicted_label = np.argmax(predictions_array) if predicted_label == true_label: color = 'blue' else: color = 'red' plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label], 100*np.max(predictions_array), class_names[true_label]), color=color) def plot_value_array(i, predictions_array, true_label): predictions_array, true_label = predictions_array, true_label[i] plt.grid(False) plt.xticks(range(10)) plt.yticks([]) thisplot = plt.bar(range(10), predictions_array, color="#777777") plt.ylim([0, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color('red') thisplot[true_label].set_color('blue') i = 0 plt.figure(figsize=(6,3)) plt.subplot(1,2,1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(1,2,2) plot_value_array(i, predictions[i], test_labels) plt.show() i = 12 plt.figure(figsize=(6,3)) plt.subplot(1,2,1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(1,2,2) plot_value_array(i, predictions[i], test_labels) plt.show() num_rows = 5 num_cols = 3 num_images = num_rows*num_cols plt.figure(figsize=(2*2*num_cols, 2*num_rows)) for i in range(num_images): plt.subplot(num_rows, 2*num_cols, 2*i+1) plot_image(i, predictions[i], test_labels, test_images) plt.subplot(num_rows, 2*num_cols, 2*i+2) plot_value_array(i, predictions[i], test_labels) plt.tight_layout() plt.show()
猫狗
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers, regularizers import numpy as np import os import cv2 import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "1" resize = 224 path ="D:/anacana/train" def load_data(): imgs = os.listdir(path) num = len(imgs) train_data = np.empty((5000, resize, resize, 3), dtype="int32") train_label = np.empty((5000, ), dtype="int32") test_data = np.empty((5000, resize, resize, 3), dtype="int32") test_label = np.empty((5000, ), dtype="int32") for i in range(5000): if i % 2: train_data[i] = cv2.resize(cv2.imread(path+'/'+ 'dog.' + str(i) + '.jpg'), (resize, resize)) train_label[i] = 1 else: train_data[i] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resize)) train_label[i] = 0 for i in range(5000, 10000): if i % 2: test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'dog.' + str(i) + '.jpg'), (resize, resize)) test_label[i-5000] = 1 else: test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resize)) test_label[i-5000] = 0 return train_data, train_label, test_data, test_label def vgg16(): weight_decay = 0.0005 nb_epoch = 100 batch_size = 32 # layer1 model = keras.Sequential() model.add(layers.Conv2D(64, (3, 3), padding='same', input_shape=(224, 224, 3), kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.3)) # layer2 model.add(layers.Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer3 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer4 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer5 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer6 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer7 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer8 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer9 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer10 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer11 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer12 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.Dropout(0.4)) # layer13 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) model.add(layers.Dropout(0.5)) # layer14 model.add(layers.Flatten()) model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) # layer15 model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.BatchNormalization()) # layer16 model.add(layers.Dropout(0.5)) model.add(layers.Dense(2)) model.add(layers.Activation('softmax')) return model #if __name__ == '__main__': train_data, train_label, test_data, test_label = load_data() train_data = train_data.astype('float32') test_data = test_data.astype('float32') train_label = keras.utils.to_categorical(train_label, 2) test_label = keras.utils.to_categorical(test_label, 2) #定义训练方法,超参数设置 model = vgg16() sgd = tf.keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) #设置优化器为SGD model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) history = model.fit(train_data, train_label, batch_size=20, epochs=10, validation_split=0.2, #把训练集中的五分之一作为验证集 shuffle=True) scores = model.evaluate(test_data,test_label,verbose=1) print(scores) model.save('model/vgg16dogcat.h5') acc = history.history['accuracy'] # 获取训练集准确性数据 val_acc = history.history['val_accuracy'] # 获取验证集准确性数据 loss = history.history['loss'] # 获取训练集错误值数据 val_loss = history.history['val_loss'] # 获取验证集错误值数据 epochs = range(1, len(acc) + 1) plt.plot(epochs, acc, 'bo', label='Trainning acc') # 以epochs为横坐标，以训练集准确性为纵坐标 plt.plot(epochs, val_acc, 'b', label='Vaildation acc') # 以epochs为横坐标，以验证集准确性为纵坐标 plt.legend() # 绘制图例，即标明图中的线段代表何种含义 plt.show()
pytorch
import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 1 input image channel, 6 output channels, 5x5 square convolution # kernel self.conv1 = nn.Conv2d(1, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) # an affine operation: y = Wx + b self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): # Max pooling over a (2, 2) window x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # If the size is a square you can only specify a single number x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def num_flat_features(self, x): size = x.size()[1:] # all dimensions except the batch dimension num_features = 1 for s in size: num_features *= s return num_features net = Net() print(net)
浙公网安备 33010602011771号