I need to modify output of last feature map of my second convolution layer.
Or add array to my conv layer output if it's possible.
Below is python script i created and example of desired change in output.
Thank you for your help!
import numpy as np
from keras import backend as K
num=18
m=11
n=50
k=3
np.random.seed(100)
features = np.random.rand(num,m,n,k)
input_shape=features.shape[1:]
model = Sequential()
model.add(Conv2D(2, kernel_size=(1, 3), strides=(1,1),activation='relu',input_shape=input_shape))
model.add(Conv2D(21, kernel_size=(1, 48), strides=(1,1),padding="valid",activation='relu'))
model.add(Conv2D(1, kernel_size=(1, 1), strides=(1, 1),activation='relu',padding="valid"))
model.add(Dense(1, activation='softmax'))
Adam = optimizers.Adam(lr=0.00003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='mse',optimizer=Adam)
get_1st_layer_output = K.function([model.layers[0].input],
[model.layers[1].output])
layer_output = get_1st_layer_output([features])
Setting DESIRED layer_output values
I need to do it every propagation step.
for i in range(0,11):
layer_output[0][0][i][0][20]=0.1
print(layer_output[0][0][i][0][20])
I think I would use a concatenation with a constant tensor in that case. Unfortunately, I can't quite get it to work, but I'll share my work anyway to hopefully help you on your way.
import numpy as np
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Conv2D, Dense, Concatenate
from keras import optimizers
num=18
m=11
n=50
k=3
np.random.seed(100)
features = np.random.rand(num, m, n, k)
custom_tensor = K.constant(0.1, shape=(11, 48, 1))
input_shape = features.shape[1:]
input = keras.Input(shape=input_shape)
print(K.ndim(input))
layer0 = Conv2D(2, kernel_size=(1, 3), strides=(1,1),activation='relu')(input)
layer0_added = Concatenate(axis=-1)([layer0, custom_tensor])
layer1 = Conv2D(20, kernel_size=(1, 48), strides=(1,1),padding="valid",activation='relu')(layer0_added)
layer2 = Conv2D(1, kernel_size=(1, 1), strides=(1, 1),activation='relu',padding="valid")(layer1)
layer3 = Dense(1, activation='softmax')(layer2)
model = keras.models.model(layer0)
Adam = optimizers.Adam(lr=0.00003, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='mse', optimizer=Adam)
It produced an error
ValueError: `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 11, 48, 2), (11, 48, 1)]
But hopefully this helps you along anyway.