network with Xception as input plus 1 dense (37 outputs), LR with scheduler (starting from 1e-3), Adam optimizer and 'MSE' as loss function
We split training dataset into test set (20%) and train set (80%), using it 10% for validation and 90% for training, obtaining these following results:
here is the NN used:
def CNN_galaxy(model):
inp = model
model = Sequential()
model.add(inp)
model.add(Flatten())
model.add(Dropout(0.25))
model.add(Dense(NUM_CLASSES))
model.add(Activation('sigmoid'))
for layer in model.layers:
layer.trainable = True
print("compiling the model...")
optimizer = tf.keras.optimizers.Adam(lr=LR, decay=WEIGHT_DECAY)
model.compile(optimizer, loss='mse', metrics=[root_mean_squared_error])
return model
img_shape = (224, 224, 3)
xception_model = tf.keras.applications.xception.Xception(include_top=False, input_shape=img_shape)
net = CNN_galaxy(xception_model)