Пишу нейросеть, а она ни в какую не обучается

import os
import tensorflow as tf
import numpy as np
import cv2
from matplotlib import pyplot as plt
 
x = np.array([
    cv2.cvtColor(cv2.resize(cv2.imread(f"NZ/{i}"), (128, 128)), cv2.COLOR_BGR2GRAY) for i in os.listdir("NZ")
] + [
    cv2.cvtColor(cv2.resize(cv2.imread(f"Z/{i}"), (128, 128)), cv2.COLOR_BGR2GRAY) for i in os.listdir("Z")
])
#x=np.float64(x)
#x/=255.0
#x /= 255
y = np.array([
    0 for i in range(len(os.listdir("NZ")))
] + [
    1 for i in range(len(os.listdir("Z")))
])
 
print(y, len(y))
 
def unison_shuffled_copies(a, b):
    assert len(a) == len(b)
    p = np.random.permutation(len(a))
    return a[p], b[p]
 
x, y = unison_shuffled_copies(x, y)
print(y)
 
print(x[0])
#plt.imshow(x[0])
print(y[0])
plt.axis('off')
plt.title('test')
plt.show()
 
input_shape = (128, 128, 1)
 
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(256, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(512, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(1024, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
 
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 
model.summary()
 
 
histroy = model.fit(x=x, y=y, batch_size=30, epochs=70, shuffle=True, validation_split=0.3)
plt.plot(histroy.history['accuracy'], color = "blue")
plt.plot(histroy.history['val_accuracy'], color = "red")
plt.show()