0 validation loss while training TensorFlow CNN model - tensorflow

Question

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
DATADIR = "C:\\Users\\John\\Documents\\CroppedYale"
CATEGORIES = ["yaleB01","yaleB02","yaleB03","yaleB04","yaleB05","yaleB06","yaleB07","yaleB08","yaleB09","yaleB10","yaleB11","yaleB12","yaleB13","yaleB15","yaleB16","yaleB17","yaleB18","yaleB19","yaleB20","yaleB21","yaleB22","yaleB23","yaleB24","yaleB25","yaleB26","yaleB27","yaleB28","yaleB29","yaleB30","yaleB31","yaleB32","yaleB33","yaleB34","yaleB35","yaleB36","yaleB37","yaleB38","yaleB39"]
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array, cmap="gray")
plt.show()
break
break
print(img_array)
IMG_SIZE = 50
new_array = cv2.resize(img_array, (IMG_SIZE,IMG_SIZE))
plt.imshow(new_array,cmap="gray")
plt.show()
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE,IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
create_training_data()
print(len(training_data))
import random
random.shuffle(training_data)
for sample in training_data[:10]:
print(sample[1])
X = []
y = []
for features, label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y = np.array(y)
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
X = X/255.0
model = Sequential()
model.add( (Conv2D(64 , (3,3) , input_shape = X.shape[1:])))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64 , (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation("relu"))
model.add(Dense(1))
model.add(Activation("sigmoid"))
model.compile(loss="CategoricalCrossentropy",
optimizer="adam",
metrics=['accuracy'])
model.fit(X, y, batch_size=32, epochs=10, validation_split=0.1)
Does anyone know why I get 0.00 loss? Obviously I'm doing something wrong. I just don't know what. It's 40 people with their own folder each folder has 36 images of their face. Is it a problem with how I processed my data or the model itself? Maybe the model has wrong layers or the preprocessing is wrong altogether.