When i train a network with keras, why are the shape of my predictions not accurate?

Question

I'm tensorflow keras to train a model to classify if an image is an a or b. I have 20,000 randomly generated images to use for training (half a, half b). example of a image example of b image

First I import necessary packages

import tensorflow
from matplotlib import pyplot as plt
import cv2
from matplotlib import pyplot as plt
import random
from tensorflow.keras import models 
from tensorflow.keras import layers 
import numpy as np

After that, I load the images from my folder, and process them, turning them into arrays of only 0 and 1, save them together with an appropriate label of either 1 if the image is an a, or 0 if the image is a b. Once I have done that, I put them in one list and shuffle the list to make it random.

a_letters = []
b_letters = []

folder_path_a = 'C:/path/to/folder/'
folder_path_b = 'C:/path/to/folder/'

count = 0
while count < 10000:
    path = folder_path_a + f'a{count}.png'
    img = cv2.imread(path)
    gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    for row_number, row in enumerate(gray_image):
        for column_number, column in enumerate(row):
            if gray_image[row_number][column_number] > 50:
                gray_image[row_number][column_number] = 1
            else:
                gray_image[row_number][column_number] = 0
    #gray_image = np.expand_dims(gray_image, axis=2)
    image_and_label = [gray_image, 1]
    a_letters.append(image_and_label)
    count = count + 1

    
count = 0    
while count < 10000:
    path = folder_path_b + f'b{count}.png'
    img = cv2.imread(path)
    gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    for row_number, row in enumerate(gray_image):
        for column_number, column in enumerate(row):
            if gray_image[row_number][column_number] > 50:
                gray_image[row_number][column_number] = 1
            else:
                gray_image[row_number][column_number] = 0
    # gray_image = np.expand_dims(gray_image, axis=2)
    image_and_label = [gray_image, 0]
    b_letters.append(image_and_label)    
    count = count + 1    

    
unified_list = a_letters + b_letters
random.shuffle(unified_list)

Next, I separate the labels and images into their own lists and split them into training and validation data.

images = []
labels = []

for image, label in unified_list:
    images.append(image)
    labels.append(float(label))

x_train = images[:15000]
y_train = labels[:15000]

x_val = images[15000:]
y_val = labels[15000:]

Then I convert the lists into numpy arrays, and expand the dimensions of the labels (before, i tried to train the model, i got an error saying logits and labels need to be the same dimension, so I have expanded the dimension of the labels to make them the same dimension of the image)

x_train_array = np.asarray(x_train)
y_train_array = np.asarray(y_train)

x_val_array = np.asarray(x_val)
y_val_array = np.asarray(y_val)

y_train_array = np.expand_dims(y_train_array, axis =1)
y_val_array = np.expand_dims(y_val_array, axis = 1)

Next, I build a model and train it:

model = models.Sequential()
model.add(layers.Dense(512, activation='relu', input_shape=(169,191,)))
model.add(layers.Dense(150, activation='relu'))
model.add(layers.Dense(250, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

model.fit(x_train_array, y_train_array, epochs=10, batch_size=500, validation_data=(x_val_array, y_val_array))

Here's the model summary: model summary

When I try to get predictions with my model by using this code:

predictions = model.predict(x_val_array)

I get a predictions.shape of (5000, 169, 1). It seems that instead of getting one prediction per image, I'm getting 169? I've been working on this for a while and I can't seem to figure it out.