学习笔记TF054:TFLearn、Keras
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元框架(metaframework)。
TFLearn。模块化深度学习框架,更高级API,快速实验,完全透明兼容。
TFLearn实现AlexNet。
https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
牛津大学鲜花数据集(Flower Dataset)。http://www.robots.ox.ac.uk/~vgg/data/flowers/17/ 。提供17个类别鲜花数据,每个类别80张图片,有大量姿态、光变化。
# -*- coding: utf-8 -*-""" AlexNet.Applying 'Alexnet' to Oxford's 17 Category Flower Dataset classification task.References: - Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS, 2012. - 17 Category Flower Dataset. Maria-Elena Nilsback and Andrew Zisserman.Links: - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - [Flower Dataset (17)](http://www.robots.ox.ac.uk/~vgg/data/flowers/17/)"""from __future__ import division, print_function, absolute_importimport tflearnfrom tflearn.layers.core import input_data, dropout, fully_connectedfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.normalization import local_response_normalizationfrom tflearn.layers.estimator import regressionimport tflearn.datasets.oxflower17 as oxflower17# 加载数据X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))# Building 'AlexNet' 构建网络模型network = input_data(shape=[None, 227, 227, 3])network = conv_2d(network, 96, 11, strides=4, activation='relu')network = max_pool_2d(network, 3, strides=2)network = local_response_normalization(network)network = conv_2d(network, 256, 5, activation='relu')network = max_pool_2d(network, 3, strides=2)network = local_response_normalization(network)network = conv_2d(network, 384, 3, activation='relu')network = conv_2d(network, 384, 3, activation='relu')network = conv_2d(network, 256, 3, activation='relu')network = max_pool_2d(network, 3, strides=2)network = local_response_normalization(network)network = fully_connected(network, 4096, activation='tanh')network = dropout(network, 0.5)network = fully_connected(network, 4096, activation='tanh')network = dropout(network, 0.5)network = fully_connected(network, 17, activation='softmax')network = regression(network, optimizer='momentum',loss='categorical_crossentropy',learning_rate=0.001)# Training 训练模型 载入AlexNet模型检查点文件model = tflearn.DNN(network, checkpoint_path='model_alexnet',max_checkpoints=1, tensorboard_verbose=2)model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True,show_metric=True, batch_size=64, snapshot_step=200,snapshot_epoch=False, run_id='alexnet_oxflowers17')
Keras。高级Python神经网络框架。https://keras.io/。TensorFlow默认框架。快速搭建原型。兼容Theano和TensorFlow。Keras高度封装,适合新手,代码更新快,示例代码多,文档、讨论区完善。自动调用GPU并行计算。模块化,模型神经层、成本函数、优化器、初始化、激活函数、规范化模块,组合创建模型。极简。易扩展,容易添加新模块。Python语言。
Keras模型。Keras核心数据结构是模型。模型组织网络层。Sequential模型,网络层顺序构成栈,单输入单输出,层间只有相邻关系,简单模型。Model模型建立复杂模型。
Sequential模型。加载完数据,X_train,Y_train,X_test,Y_test。构建模型:
from keras.models import Sequential
from keras.layers.core import Dense, Dropout,Activation
model = Sequential() model.add(Dense(output_dim=64, input_dim=100)) model.add(Activation('relu')) model.add(Dense(output_dim=10)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics='accuracy')model.fit(X_train, Y_train, batch_size=32, nb_epoch=5)loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
Keras源码示例 https://github.com/fchollet/keras :CIFAR10-图片分类(CNN、实时数据)、IMDB-电影评论观点分类(LSTM)、Reuters-新闻主题分类(多层感知器)、MNIST-手写数字识别(多层感知器、CNN)、OCR-识别字符级文本生成(LSTM)。
安装。pip install keras 。选择依赖后端,~/.keras/keras.json 修改最后一行”backend”:”fensorflow” 。
Keras实现卷积神经网络。
https://github.com/fchollet/blob/master/examples/mnist_cnn.py 。
#!/usr/bin/python# -*- coding:utf8 -*-'''Trains a simple convnet on the MNIST dataset.Gets to 99.25% test accuracy after 12 epochs(there is still a lot of margin for parameter tuning).16 seconds per epoch on a GRID K520 GPU.'''from __future__ import print_functionimport kerasfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras import backend as Kbatch_size = 128num_classes = 10 # 分类数epochs = 12 # 训练轮数# input image dimensions 输入图片维度img_rows, img_cols = 28, 28# the data, shuffled and split between train and test sets# 加载数据(x_train, y_train), (x_test, y_test) = mnist.load_data()if K.image_data_format() == 'channels_first': #使用Theano顺序:(conv_dim1,channels,conv_dim2,conv_dim3) x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols)else: #使用TensorFlow顺序:(conv_dim1conv_dim2,conv_dim3,channels,) x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train /= 255x_test /= 255print('x_train shape:', x_train.shape)print(x_train.shape[0], 'train samples')print(x_test.shape[0], 'test samples')# convert class vectors to binary class matrices 将类向量转换为二进制类矩阵y_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)#模型构建:2个卷积层、1个池化层、2个全连接层model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(num_classes, activation='softmax'))# 模型编译model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])# 模型训练model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))# 模型评估score = model.evaluate(x_test, y_test, verbose=0)print('Test loss:', score[0])print('Test accuracy:', score[1])
模型加载保存。 https://github.com/fchollet/keras/blob/master/tests/test_model_saving.py 。
Keras的save_model、load_model方法将模型、权重保存到HDF5文件。包括模型结构、权重、训练配置(损失函数、优化器)。方便中断后再继续训练。
from keras.models import save_model, load_model
def test_sequential_model_saving(): model = Sequential() model.add(Dense(2, input_shape=(3,))) model.add(RepeatVector(3)) model.add(TimeDistributed(Dense(3))) model.compile(loss=losses.MSE, optimizer=optimizers.RMSprop(lr=0.0001), metrics=[metrics.categorical_accuracy], sample_weight_mode='temporal') x = np.random.random((1, 3)) y = np.random.random((1, 3, 3)) model.train_on_batch(x, y) out = model.predict(x) _, fname = tempfile.mkstemp('.h5') # 创建HDFS 5文件 save_model(model, fname) new_model = load_model(fname) os.remove(fname) out2 = new_model.predict(x) assert_allclose(out, out2, atol=1e-05) # test that new updates are the same with both models # 检测新保存的模型和之前定义的模型是否一致 x = np.random.random((1, 3)) y = np.random.random((1, 3, 3)) model.train_on_batch(x, y) new_model.train_on_batch(x, y) out = model.predict(x) out2 = new_model.predict(x) assert_allclose(out, out2, atol=1e-05)
只保存模型结构。
json_string = model.to_jsion()yaml_string = model.to_yaml()
手动编辑。
from keras.models import model_from_jsonmodel = model_from_json(json_string)model = model_from_yaml(yaml_string)
只保存模型权重。
model.save_weights('my_model_weights.h5')model.load_weights('my_model_weights.h5')
参考资料:
《TensorFlow技术解析与实战》
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