from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers import Conv1D, MaxPooling1D import scipy.io as sio import matplotlib.pyplot as plt from keras.utils import np_utils import keras import numpy as np from keras import regularizers from keras.callbacks import TensorBoard from keras.utils import plot_model from keras import backend as K from os.path import exists, join from os import makedirs batch_sizes = 256 nb_class = 10 nb_epochs = 2 log_dir = './bgbv2_log_dir' if not exists(log_dir): makedirs(log_dir) # input image dimensions img_rows, img_cols = 1, 2048 ''' 第一步 准备数据 ''' # matlab文件名 准备数据 file_name = u'G:/GANCode/CSWU/12k drive end vps/trainset/D/D_dataset.mat' original_data = sio.loadmat(file_name) X_train = original_data['x_train'] Y_train = original_data['y_train'] X_test = original_data['x_test'] Y_test = original_data['y_test'] channel = 1 X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], channel)) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], channel)) input_shape = (X_train.shape[1], channel) # 标签打乱 permutation = np.random.permutation(Y_train.shape[0]) X_train = X_train[permutation, :, :] Y_train = Y_train[permutation] permutation = np.random.permutation(Y_test.shape[0]) X_test = X_test[permutation, :, :] Y_test = Y_test[permutation] X_train = X_train.astype('float32') # astype SET AS TYPE INTO X_test = X_test.astype('float32') #X_train = (X_train+1)/2 #X_test = (X_test+1)/2 print('x_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') X_meta = X_test.reshape((X_test.shape[0], X_test.shape[1])) kkkkk=0 # save class labels to disk to color data points in TensorBoard accordingly with open(join(log_dir, 'metadata.tsv'), 'w') as f: np.savetxt(f, Y_test[:200]) ''' 第三步 设置标签 one-hot ''' Y_test = np_utils.to_categorical(Y_test, nb_class) # Label Y_train = np_utils.to_categorical(Y_train, nb_class) ''' 第四步 网络model ''' model = Sequential() model.add(Conv1D(64, 11, activation='relu', input_shape=(2048, 1))) model.add(Conv1D(64, 11, activation='relu')) model.add(MaxPooling1D(3)) model.add(Conv1D(128, 11, activation='relu')) model.add(Conv1D(128, 11, activation='relu')) ''' model.add(GlobalAveragePooling1D()) model.add(Dropout(0.5)) ''' model.add(MaxPooling1D(3)) model.add(Dropout(0.25)) model.add(keras.layers.Flatten()) model.add(Dense(1000, activation='relu')) model.add(Dense(100, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) embedding_layer_names = set(layer.name for layer in model.layers if layer.name.startswith('dense_')) # https://stackoverflow.com/questions/45265436/keras-save-image-embedding-of-the-mnist-data-set model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) callbacks = [keras.callbacks.TensorBoard( log_dir='bgbv2_log_dir', embeddings_layer_names=['dense_2'], #监视某一层，就要写某一层的名字，可以同时监视很多层，用上面的字典形式。 #embeddings_metadata='metadata.tsv', embeddings_freq=1, #histogram_freq=1, embeddings_data=X_test # 数据要和X_train保持一致。这里我用的是一维数据，（60000,2048,1）表示有6万个样本，每个样本有2048个长度，且每个样本有1个通道（1个传感器），换成多个通道的话，就要使用多个传感器的数据。 )] model.fit(X_train, Y_train, batch_size=batch_sizes, callbacks=callbacks, epochs=nb_epochs, verbose=1, validation_data=(X_test, Y_test)) xxasfs=1 # You can now launch tensorboard with `tensorboard --logdir=./logs` on your # command line and then go to http://localhost:6006/#projector to view the # embeddings # keras.callbacks.TensorBoard( # log_dir='./logs', # histogram_freq=0, # batch_size=32, # write_graph=True, # write_grads=False, # write_images=False, # embeddings_freq=0, # embeddings_layer_names=None, # embeddings_metadata=None, # embeddings_data=None, # update_freq='epoch')
坑死我了。
没有人教，自己琢磨了一天。
下面就能清楚地看见我们的三维图啦~用来写paper和PPT都是极好的素材。
PS：任何一个图层的输出：
https://stackoverflow.com/questions/41711190/keras-how-to-get-the-output-of-each-layer
参考1，keras Tensorboard官方说明
https://keras.io/callbacks/#tensorboard
from __future__ import print_function from os import makedirs from os.path import exists, join import keras from keras.callbacks import TensorBoard from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K import numpy as np batch_size = 128 num_classes = 10 epochs = 12 log_dir = './logs' if not exists(log_dir): makedirs(log_dir) # input image dimensions img_rows, img_cols = 28, 28 # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() if K.image_data_format() == 'channels_first': 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: 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 /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # save class labels to disk to color data points in TensorBoard accordingly with open(join(log_dir, 'metadata.tsv'), 'w') as f: np.savetxt(f, y_test) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) tensorboard = TensorBoard(batch_size=batch_size, embeddings_freq=1, embeddings_layer_names=['features'], embeddings_metadata='metadata.tsv', embeddings_data=x_test) 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', name='features')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, callbacks=[tensorboard], epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) # You can now launch tensorboard with `tensorboard --logdir=./logs` on your # command line and then go to http://localhost:6006/#projector to view the # embeddings
参考2，keras Mnist最后一层可视化。
https://keras.io/examples/tensorboard_embeddings_mnist/
参考3，IMDB影视评论最后一层可是化
import keras from keras import layers from keras.datasets import imdb from keras.preprocessing import sequence max_features = 500 # 原文为2000 max_len = 500 (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) x_train = sequence.pad_sequences(x_train, maxlen=max_len) x_test = sequence.pad_sequences(x_test, maxlen=max_len) KK=x_train[:100].astype("float32") MM=1 model = keras.models.Sequential() model.add(layers.Embedding(max_features, 128, input_length=max_len, name='embed')) model.add(layers.Conv1D(32, 7, activation='relu')) model.add(layers.MaxPooling1D(5)) model.add(layers.Conv1D(32, 7, activation='relu')) model.add(layers.GlobalMaxPooling1D()) model.add(layers.Dense(1)) model.summary() model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) callbacks = [keras.callbacks.TensorBoard( log_dir='my_log_dir', histogram_freq=1, embeddings_freq=1, embeddings_data=x_train[:100].astype("float32") )] history = model.fit(x_train, y_train, epochs=20, batch_size=128, validation_split=0.2, callbacks=callbacks)
#补充 https://codeday.me/bug/20180924/267508.html