Accuracy does not change on training

Is this related to the fact that I use Mobilenet_v2 for classifying pictures, where there are about 60 pictures in the train and 7 pictures in the test

data = Data()
train_data_gen, val_data_gen = Data.prepare_data(preprocess_input=preprocess_input, train_dir=train_dir, val_dir=test_dir)

Found 123 images belonging to 2 classes.

Found 15 images belonging to 2 classes.

class Classifier:
  def __init__(self):
    self.IMG_SHAPE = (150, 150, 3)
    # базовая модель -- MobileNet
    self.base_model = tf.keras.applications.MobileNetV2(input_shape=self.IMG_SHAPE, include_top=False, weights='imagenet')
    self.base_model.trainable = False # замораживаем всю базовую модель


  def extra_layers(self, loss='binary_crossentropy', metrics='accuracy', optimizer='adam', num_classes=None):
    if num_classes == 2:
      self.model = tf.keras.Sequential([
      self.base_model,
      tf.keras.layers.GlobalAveragePooling2D(),
      tf.keras.layers.Dense(1, activation='sigmoid')
      ])
    else:
      self.model = tf.keras.Sequential([
      self.base_model,
      tf.keras.layers.GlobalAveragePooling2D(),
      tf.keras.layers.Dense(num_classes, activation='softmax')
      ])

    self.model.compile(optimizer=optimizer, loss=loss, metrics=[metrics])

    print('!the model was built with additional layers!\n')
  
  
  def fit_train(self, epochs=10, train_data_gen=train_data_gen, val_data_gen=val_data_gen):      #change valid
    self.hist = self.model.fit_generator(
    train_data_gen,
    epochs=epochs,
    validation_data=val_data_gen)

    print('!The model has been trained!\n')


  def predict_classes(self, datage=val_data_gen):
    sample_validation_images, sample_validation_labels = next(datagen)
    self.predictions = (self.model.predict(sample_validation_images) > 0.5).astype("int32").flatten()
    self.sample_validation_images = sample_validation_images
    self.sample_validation_labels = sample_validation_labels

After training, I get the following result:

Epoch 134/300
123/123 [==============================] - 1s 11ms/step - loss: 6.4891e-07 - accuracy: 1.0000 - val_loss: 0.9736 - val_accuracy: 0.5333
Epoch 135/300
123/123 [==============================] - 1s 11ms/step - loss: 6.1259e-07 - accuracy: 1.0000 - val_loss: 0.9709 - val_accuracy: 0.5333
Epoch 136/300
123/123 [==============================] - 1s 11ms/step - loss: 5.7758e-07 - accuracy: 1.0000 - val_loss: 0.9643 - val_accuracy: 0.5333
Epoch 137/300
123/123 [==============================] - 1s 11ms/step - loss: 5.4502e-07 - accuracy: 1.0000 - val_loss: 0.9405 - val_accuracy: 0.5333
Epoch 138/300
123/123 [==============================] - 1s 11ms/step - loss: 5.0976e-07 - accuracy: 1.0000 - val_loss: 0.9808 - val_accuracy: 0.5333
Epoch 139/300
123/123 [==============================] - 1s 11ms/step - loss: 4.8050e-07 - accuracy: 1.0000 - val_loss: 0.9766 - val_accuracy: 0.5333
Epoch 140/300
123/123 [==============================] - 1s 11ms/step - loss: 4.5143e-07 - accuracy: 1.0000 - val_loss: 0.9673 - val_accuracy: 0.5333
Epoch 141/300
123/123 [==============================] - 1s 11ms/step - loss: 4.2582e-07 - accuracy: 1.0000 - val_loss: 0.9850 - val_accuracy: 0.5333
Epoch 142/300
123/123 [==============================] - 1s 11ms/step - loss: 3.9907e-07 - accuracy: 1.0000 - val_loss: 0.9781 - val_accuracy: 0.5333