Styjun

So I made a CNN that classifies two types of birds, and it worked fine. After that, I tried adding one more type, but I got weird results. I already posted this on ai stack exchange, but they said its better to ask it in here, so I am providing a link to that post.

https://ai.stackexchange.com/q/11444/23452

Here is the model code:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle
import time as time

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

pickle_in = open("C:/Users/Recep/Desktop/programlar/python/X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("C:/Users/Recep/Desktop/programlar/python/Y.pickle","rb")
Y = pickle.load(pickle_in)

X = X/255.0

node_size = 64

model_name = "agi_vs_golden-".format(time.time())


tensorboard = TensorBoard(log_dir='C:/Users/Recep/Desktop/programlar/python/logs/'.format(model_name))
file_writer = tf.summary.FileWriter('C:/Users/Recep/Desktop/programlar/python/logs/'.format(model_name, sess.graph))


model = Sequential()
model.add(Conv2D(node_size,(3,3),input_shape = X.shape[1:]))
#idk what that shape does except that and validation i have no problem 
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(node_size,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(node_size))
model.add(Activation("relu"))

model.add(Dense(1))
model.add(Activation("sigmoid"))

model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])

model.fit(X,Y,batch_size=25,epochs=8,validation_split=0.1,callbacks=[tensorboard])
# idk what the validation is and how its used but dont think it caused the problem

model.save("agi_vs_gouldian.model")

By the way, as I said in the comments of my original post, I think maybe there is a lack of training the network, or I don't have the enough data. So I tried increasing the number of epochs. It kinda get the problem, but the part that I'm curious about is what happened when I had the lower epochs?

Can anyone help me?

I am giving the tensor board graphs below.

BTW, is my data array rgb?

And how can I get rid of this local max of %70?

And since I'm a beginner to this, I don't know what validation really works, but I saw that the validation graphs stays the same in the first training that I had issues with.

You try to classify three birds with sigmoid. Sigmoid is good for binary classification. Try a softmax activation layer and see how it goes. I suggest replacing

model.add(Dense(1))
model.add(Activation("sigmoid"))

with

model.add(Dense(3, activation='softmax'))

Where 3 is the number of birds' type you want to classify.

Have a look here, a very good tutorial of using softmax as the activation layer for a multi-class classification

https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/