from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.utils.data_utils import Sequence
import os
from keras import backend as K
import numpy as np
def square(x):
return x * x;
batch_size = 64
num_classes = 10
epochs = 500
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'cifar_model2.h5'
# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train = np.round(x_train/32)/8
x_test = np.round(x_test/32)/8
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(128, (3, 3), padding='same',
input_shape=x_train.shape[1:]))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Conv2D(83, (3, 3)))
model.add(Dropout(0.25))
model.add(Activation(square))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Conv2D(130, (5, 5), padding='same'))
model.add(Activation(square))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
opt = keras.optimizers.Adam(amsgrad=True, decay=0.0001, lr = 0.001)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-06,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.,
zoom_range=0.,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=True,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
validation_split=0.0)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
checkpoint = keras.callbacks.ModelCheckpoint(model_path, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False, mode='auto', period=10)
callback_list=[checkpoint]
datagen.fit(x_train)
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
callbacks=callback_list,steps_per_epoch=len(x_train)/batch_size,
epochs=epochs, validation_data=(x_test, y_test), workers=4)
# Save
model.save(model_path)
print('Saved trained model at %s ' % model_path)
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
from numpy import loadtxt
from keras.models import load_model
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'cifar_model2.h5'
model_path = os.path.join(save_dir, model_name)
def square(x):
return x * x;
# load model
model = load_model(model_path, custom_objects={'square': square})]
接下来，我输入代码：
model.summary()
用于查看具有参数的所有层（可训练层）。它显示了各层：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 32, 32, 128) 3584
_________________________________________________________________
average_pooling2d_4 (Average (None, 16, 16, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 14, 14, 83) 95699
_________________________________________________________________
dropout_3 (Dropout) (None, 14, 14, 83) 0
_________________________________________________________________
activation_4 (Activation) (None, 14, 14, 83) 0
_________________________________________________________________
average_pooling2d_5 (Average (None, 7, 7, 83) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 7, 7, 130) 269880
_________________________________________________________________
activation_5 (Activation) (None, 7, 7, 130) 0
_________________________________________________________________
average_pooling2d_6 (Average (None, 3, 3, 130) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 3, 3, 130) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 1170) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 599552
_________________________________________________________________
dense_4 (Dense) (None, 10) 5130
_________________________________________________________________
activation_6 (Activation) (None, 10) 0
=================================================================
总参数：973,845 可训练的参数：973845 不可训练的参数：0
现在的问题是如何获取每个图层的权重和偏差，然后将它们保存到一个“ csv”文件中？我刚刚使用下面的代码设法获得了单个图层的权重和偏差： / p>
from numpy import loadtxt
from keras.models import load_model
from keras.engine import InputLayer
output_center=2
input_kernel_range = range(0,10)
input_shape=[14,14,83]
input_maps = input_shape[2]
model_ = Sequential()
model_.add(AveragePooling2D(pool_size=(2, 2),input_shape=[14,14,83]))
model_.add(Conv2D(130, (5, 5), padding='same'))
model_.layers[1].set_weights(model.layers[6].get_weights())
test = np.zeros(input_shape)[np.newaxis,...]
bias = model_.predict(test)[0,output_center,output_center,:]
A = 0
for c in range(0, input_maps):
for y in input_kernel_range:
for x in input_kernel_range:
test = np.zeros(input_shape)[np.newaxis,...]
test[0,x,y,c]=1
prediction = model_.predict(test)
d = prediction[0,output_center,output_center,:] - bias
if (isinstance(A, int)):
A = d
else:
A = np.c_[A, d]
A.tofile("weights.csv", sep=',')
bias.tofile("bias.csv", sep=',')
这仅适用于一层。就像我说的那样，我正在尝试获取权重和偏差并将其保存在“ csv”中，以用于所有卷积和密集层。有人可以帮助吗？
预先感谢
答案 0 :(得分：0)
要获取Conv2D和Dense图层的权重和偏差，您可以执行以下操作：
wt = open('weights.csv', 'w')
bs = open('biases.csv', 'w')
for idx,i in enumerate(model.layers):
if(isinstance(i, Conv2D) or isinstance(i, Dense)):
weights = i.get_weights()[0]
biases = i.get_weights()[1]
weights = weights.flatten()
biases = biases.flatten()
print(weights.shape, biases.shape)
wt.write(','.join(map(str,weights.tolist()))+"\n")
bs.write(','.join(map(str,biases.tolist()))+"\n")
#np.savetxt("weight" + str(idx)+".csv" , weights , fmt='%s', delimiter=',')
#np.savetxt("bias" + str(idx) +".csv" , biases , fmt='%s', delimiter=',')
您还可以跟踪图层的原始形状，因为它已展平存储在CSV中。如果您以后希望再次加载这些权重，将会为您提供帮助。