I want to define lambda layer to combine features with cross product, then merge those models,just like the fig. ,What should I do?
Test model_1, get 128 dimensions form dense, use pywt
get two 64 dimensions feature(cA,cD
), then return cA*cD //of course I want to combine two models ,but try model_1 first.
from keras.models import Sequential,Model
from keras.layers import Input,Convolution2D,MaxPooling2D
from keras.layers.core import Dense,Dropout,Activation,Flatten,Lambda
import pywt
def myFunc(x):
(cA, cD) = pywt.dwt(x, 'db1')
# x=x*x
return cA*cD
batch_size=32
nb_classes=3
nb_epoch=20
img_rows,img_cols=200,200
img_channels=1
nb_filters=32
nb_pool=2
nb_conv=3
inputs=Input(shape=(1,img_rows,img_cols))
x=Convolution2D(nb_filters,nb_conv,nb_conv,border_mode='valid',
input_shape=(1,img_rows,img_cols),activation='relu')(inputs)
x=Convolution2D(nb_filters,nb_conv,nb_conv,activation='relu')(x)
x=MaxPooling2D(pool_size=(nb_pool,nb_pool))(x)
x=Dropout(0.25)(x)
x=Flatten()(x)
y=Dense(128,activation='relu')(x)
cross=Lambda(myFunc,output_shape=(64,))(y)
predictions=Dense(nb_classes,activation='softmax')(cross)
model = Model(input=inputs, output=predictions)
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
model.fit(X_train,Y_train,batch_size=batch_size,nb_epoch=nb_epoch,
verbose=1,validation_data=(X_test,Y_test))
Sorry, can I ask a question about tensor?
import tensorflow as tf
W1 = tf.Variable(np.array([[1,2],[3,4]]))
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
array = W1.eval(sess)
print (array)
That's right! However,
from keras import backend as K
import numpy as np
kvar=K.variable(np.array([[1,2],[3,4]]))
K.eval(kvar)
print(kvar)
I got <CudaNdarrayType(float32, matrix)>
and kvar.eval()
I got b'CudaNdarray([[ 1. 2.]
[ 3. 4.]])'
. I use keras, so how can I get array like tensorflow using keras?
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