conv1d UpSampling1D aotoencoder 自编码代码摘录

 

https://www.quora.com/How-do-I-implement-a-1D-Convolutional-autoencoder-in-Keras-for-numerical-dataset

    # ENCODER
    input_sig = Input(batch_shape=(None,128,1))
    x = Conv1D(64,3, activation='relu', padding='valid')(input_sig)
    x1 = MaxPooling1D(2)(x)
    x2 = Conv1D(32,3, activation='relu', padding='valid')(x1)
    x3 = MaxPooling1D(2)(x2)
    flat = Flatten()(x3)
    encoded = Dense(32,activation = 'relu')(flat)
     
    print("shape of encoded {}".format(K.int_shape(encoded)))
     
    # DECODER 
    x2_ = Conv1D(32, 3, activation='relu', padding='valid')(x3)
    x1_ = UpSampling1D(2)(x2_)
    x_ = Conv1D(64, 3, activation='relu', padding='valid')(x1_)
    upsamp = UpSampling1D(2)(x_)
    flat = Flatten()(upsamp)
    decoded = Dense(128,activation = 'relu')(flat)
    decoded = Reshape((128,1))(decoded)
     
    print("shape of decoded {}".format(K.int_shape(decoded)))
     
    autoencoder = Model(input_sig, decoded)
    autoencoder.compile(optimizer='adam', loss='mse', metrics=['accuracy'])

 

http://qaru.site/questions/418926/keras-autoencoder-error-when-checking-target

from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D
from keras.models import Model
from keras import backend as K
import scipy as scipy
import numpy as np 

mat = scipy.io.loadmat('edata.mat')
emat = mat['edata']

input_img = Input(shape=(64,1))  # adapt this if using `channels_first` image data format

x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x)

x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x)
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((4))(x)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

x_train = emat[:,0:80000]
x_train = np.reshape(x_train, (x_train.shape[1], 64, 1))
x_test = emat[:,80000:120000]
x_test = np.reshape(x_test, (x_test.shape[1], 64, 1))

from keras.callbacks import TensorBoard

autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test),
                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

貌似上面的有问题，修改后是：

input_img = Input(shape=(64,1))  

x = Conv1D(32, (9), activation='relu', padding='same')(input_img)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = MaxPooling1D((4), padding='same')(x)
x = Conv1D(8, (9), activation='relu', padding='same')(x)
encoded = MaxPooling1D(4, padding='same')(x)

x = Conv1D(8, (9), activation='relu', padding='same')(encoded)
x = UpSampling1D((4))(x)
x = Conv1D(16, (9), activation='relu', padding='same')(x)
x = UpSampling1D((4))(x) 
x = Conv1D(32, (9), activation='relu')(x)
x = UpSampling1D((8))(x)              ##   <-- change here (was 4)
decoded = Conv1D(1, (9), activation='sigmoid', padding='same')(x)  

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

一些生成模型：https://www.programcreek.com/python/example/93306/keras.layers.convolutional.UpSampling1D

def generator_model():  # CDNN Model
    print(INPUT_LN, N_GEN_l, CODE_LN) 

    model = Sequential()

    model.add(Convolution1D(16, 5, border_mode='same', input_shape=(CODE_LN, 1)))
    model.add(Activation('relu'))

    model.add(UpSampling1D(length=N_GEN_l[0]))
    model.add(Convolution1D(32, 5, border_mode='same'))
    model.add(Activation('relu'))

    model.add(UpSampling1D(length=N_GEN_l[1]))
    model.add(Convolution1D(1, 5, border_mode='same'))
    model.add(Activation('tanh'))
    return model 

 

def generator_model(noise_dim=100, aux_dim=47, model_name="generator"):
    # Merge noise and auxilary inputs
    gen_input = Input(shape=(noise_dim,), name="noise_input")
    aux_input = Input(shape=(aux_dim,), name="auxilary_input")
    x = concatenate([gen_input, aux_input], axis=-1)

    # Dense Layer 1
    x = Dense(10 * 100)(x) 
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 10*100

    # Reshape the tensors to support CNNs
    x = Reshape((100, 10))(x) # shape is 100 x 10

    # Conv Layer 1
    x = Conv1D(filters=250, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 250
    x = UpSampling1D(size=2)(x) # output shape is 200 x 250

    # Conv Layer 2
    x = Conv1D(filters=100, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 200 x 100
    x = UpSampling1D(size=2)(x) # output shape is 400 x 100
    
    # Conv Layer 3
    x = Conv1D(filters=1, kernel_size=13, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('tanh')(x) # final output shape is 400 x 1

    generator_model = Model(
        outputs=[x], inputs=[gen_input, aux_input], name=model_name)

    return generator_model 

 

def generator_model_44():  # CDNN Model
    model = Sequential()

    model.add(Convolution1D(16, 5, border_mode='same', input_shape=(CODE_LN, 1)))
    model.add(Activation('relu'))

    model.add(UpSampling1D(length=4))
    model.add(Convolution1D(32, 5, border_mode='same'))
    model.add(Activation('relu'))

    model.add(UpSampling1D(length=4))
    model.add(Convolution1D(1, 5, border_mode='same'))
    # model.add(Activation('relu'))
    return model 

 

def generator_model(noise_dim=100, aux_dim=47, model_name="generator"):
    # Merge noise and auxilary inputs
    gen_input = Input(shape=(noise_dim,), name="noise_input")
    aux_input = Input(shape=(aux_dim,), name="auxilary_input")
    x = merge([gen_input, aux_input], mode="concat", concat_axis=-1)

    # Dense Layer 1
    x = Dense(10 * 100)(x) 
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 10*100

    # Reshape the tensors to support CNNs
    x = Reshape((100, 10))(x) # shape is 100 x 10

    # Conv Layer 1
    x = Convolution1D(nb_filter=250,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 250
    x = UpSampling1D(length=2)(x) # output shape is 200 x 250

    # Conv Layer 2
    x = Convolution1D(nb_filter=100,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x) # output shape is 200 x 100
    x = UpSampling1D(length=2)(x) # output shape is 400 x 100
    
    
    # Conv Layer 3
    x = Convolution1D(nb_filter=1,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = BatchNormalization()(x)
    x = Activation('tanh')(x) # final output shape is 400 x 1

    generator_model = Model(
        input=[gen_input, aux_input], output=[x], name=model_name)

    return generator_model 

 

 

下面代码有问题，但是结构可以参考（https://groups.google.com/forum/#!topic/keras-users/EXOuZ4YKONY）：

import numpy as np

from keras.layers import Input # define the input shape for the model
from keras.layers import Conv1D, MaxPooling1D, UpSampling1D # for the convnet structure
from keras.models import Model # for the overall definition


from keras.initializers import Constant # bias initialisation
from keras.initializers import TruncatedNormal # kernel initialissation
from keras.layers.advanced_activations import LeakyReLU # activation function (from NSynth)


# define input shape
input_img = Input(shape=(500,128))
print('Some information about tensor expected shapes')
print('Input tensor shape:', input_img.shape)


# define encoder convnet
# obs: 1D convolution implemented
x = Conv1D(filters=128,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=4,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(input_img)
x = Conv1D(filters=256,kernel_size=(4),activation=LeakyReLU(),padding='causal',dilation_rate=2,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
x = MaxPooling1D(pool_size=4,strides=4)(x)
encoded = Conv1D(filters=512,kernel_size=4,activation=LeakyReLU(),padding='causal',bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
print('Encoded representation tensor shape:', encoded.shape)


# define decoder convnet
x = Conv1D(filters=256,kernel_size=4,activation=LeakyReLU(),padding='causal',bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(encoded)
x = UpSampling1D(size=4)(x)
x = Conv1D(filters=128,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=2,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
decoded = Conv1D(filters=1,kernel_size=4,activation=LeakyReLU(),padding='causal',dilation_rate=4,bias_initializer=Constant(0.1),kernel_initializer=TruncatedNormal())(x)
print('Decoded representation tensor shape:', decoded.shape)


# define overal autoencoder model
cae = Model(inputs=input_img, outputs=decoded)
cae.compile(optimizer='adam', loss='mse')

# check for equal size
# obs: the missing value is the batch_size
if input_img.shape[1:] != decoded.shape[1:]: print('alert: in/out dimension mismatch')

And, with no surprise, I get
alert: in/out dimension mismatch