【keras】用tensorboard监视CNN每一层的输出

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')

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

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