I am currently trying to build a self adjusting network, such that given any number of inputs, should always provide an output of shape (15,145)
The network structure is pretty simple and looks like this:
inputs = 36
list_of_input = [Input(shape = (45,5,3)) for i in range(inputs)]
list_of_conv_output = []
list_of_max_out = []
for i in range(splits):
list_of_conv_output.append(Conv2D(filters = 145 , kernel_size = (30,3))(list_of_input[i]))
list_of_max_out.append((MaxPooling2D(pool_size=(3,2))(list_of_conv_output[i])))
merge = keras.layers.concatenate(list_of_max_out)
#reshape = Reshape((merge.shape[0],merge.shape[3]))(merge)
dense1 = Dense(units = 1000, activation = 'relu', name = "dense_1")(merge)
dense2 = Dense(units = 1000, activation = 'relu', name = "dense_2")(dense1)
dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2)
model = Model(inputs = list_of_input , outputs = dense3)
model.compile(loss="sparse_categorical_crossentropy", optimizer="adam")
print model.summary()
raw_input("SDasd")
hist_current = model.fit(x = [train_input[i] for i in range(100)],
y = labels_train_data,
shuffle=False,
validation_data=([test_input[i] for i in range(10)], labels_test_data),
validation_split=0.1,
epochs=150000,
batch_size = 15,
verbose=1)
It been adjusted for having 36 inputs which would given it an output shape of (15,1,145) - but how can i determine the number of filters, kernel size and pooling size that would give me the desired output size. The network is supposed to be used for classification, and the output vector of length 15 with classes for each third entry in the first axis (45 = 15*3). the total number of classes is 145, hence output dimension (15,145)
