Pythonのmodel.compileでTypeError：Nonetypeが起きました。

前提・実現したいこと

Pythonで画像認識を用いてじゃんけんの手の形を判別するシステムをあるサイトで見つけて
サイトの通りに実行したところエラーが発生しました
■■な機能を実装中に以下のエラーメッセージが発生しました。

発生している問題・エラーメッセージ

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-24-6c44726a183e> in <module>()
----> 1 model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])

C:\Anaconda3\envs\Sotsuken\lib\site-packages\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
    227         #                   loss_weight_2 * output_2_loss_fn(...) +
    228         #                   layer losses.
--> 229         self.total_loss = self._prepare_total_loss(masks)
    230 
    231         # Functions for train, test and predict will

C:\Anaconda3\envs\Sotsuken\lib\site-packages\keras\engine\training.py in _prepare_total_loss(self, masks)
    690 
    691                     output_loss = loss_fn(
--> 692                         y_true, y_pred, sample_weight=sample_weight)
    693 
    694                 if len(self.outputs) > 1:

C:\Anaconda3\envs\Sotsuken\lib\site-packages\keras\losses.py in __call__(self, y_true, y_pred, sample_weight)
     71             losses = self.call(y_true, y_pred)
     72             return losses_utils.compute_weighted_loss(
---> 73                 losses, sample_weight, reduction=self.reduction)
     74 
     75     @classmethod

C:\Anaconda3\envs\Sotsuken\lib\site-packages\keras\utils\losses_utils.py in compute_weighted_loss(losses, sample_weight, reduction, name)
    164         # Update dimensions of `sample_weight` to match with `losses` if possible.
    165         losses, _, sample_weight = squeeze_or_expand_dimensions(
--> 166             losses, None, sample_weight)
    167 
    168         # Broadcast weights if possible.

C:\Anaconda3\envs\Sotsuken\lib\site-packages\keras\utils\losses_utils.py in squeeze_or_expand_dimensions(y_pred, y_true, sample_weight)
     74         if y_pred_rank == 0 and weights_rank == 1:
     75             y_pred = K.expand_dims(y_pred, -1)
---> 76         elif weights_rank - y_pred_rank == 1:
     77             sample_weight = K.squeeze(sample_weight, -1)
     78         elif y_pred_rank - weights_rank == 1:

TypeError: unsupported operand type(s) for -: 'int' and 'NoneType'

該当のソースコード

Python
1import keras
2from keras import layers
3from keras import models
4from keras import optimizers
5from keras.preprocessing.image import ImageDataGenerator
6import matplotlib.pyplot as plt
7import tensorflowjs as tfjs
8
9model = models.Sequential()
10model.add(layers.Conv2D(32, (3, 3), activation='relu',
11                        input_shape=(100, 100, 3)))
12model.add(layers.MaxPooling2D((2, 2)))
13model.add(layers.Conv2D(64, (3, 3), activation='relu'))
14model.add(layers.MaxPooling2D((2, 2)))
15model.add(layers.Conv2D(128, (3, 3), activation='relu'))
16model.add(layers.MaxPooling2D((2, 2)))
17model.add(layers.Conv2D(128, (3, 3), activation='relu'))
18model.add(layers.MaxPooling2D((2, 2)))
19model.add(layers.Flatten())
20model.add(layers.Dropout(0.5))
21model.add(layers.Dense(512, activation='relu'))
22model.add(layers.Dense(10, activation='softmax'))
23
24model.compile(loss='categorical_crossentropy',
25              optimizer='adam',metrics=['acc'])
26
27classes = ['zero', 'one', 'two', 'three', 'four',
28           'five', 'seven', 'eight', 'nine']
29
30train_dir = 'hand_sign_digit_data/train'
31validation_dir = 'hand_sign_digit_data/validation'
32
33train_datagen = ImageDataGenerator(
34    rescale=1./255,
35    rotation_range=40,
36    width_shift_range=0.2,
37    height_shift_range=0.2,
38    shear_range=0.2,
39    zoom_range=0.2,
40    horizontal_flip=True,)
41
42test_datagen = ImageDataGenerator(rescale=1./255)
43
44train_generator = train_datagen.flow_from_directory(
45        train_dir,
46        target_size=(100, 100),
47        batch_size=32,
48        class_mode='categorical')
49
50validation_generator = test_datagen.flow_from_directory(
51        validation_dir,
52        target_size=(100, 100),
53        batch_size=32,
54        class_mode='categorical')
55
56history = model.fit_generator(
57      train_generator,
58      steps_per_epoch=100,
59      epochs=100,
60      validation_data=validation_generator,
61      validation_steps=10)
62
63model.save('sign_language_vgg16_1.h5')
64
65#convert the vgg16 model into tf.js model
66save_path = '../nodejs/static/sign_language_vgg16'
67tfjs.converters.save_keras_model(model, save_path)
68print("[INFO] saved tf.js vgg16 model to disk..")
69
70acc = history.history['acc']
71val_acc = history.history['val_acc']
72loss = history.history['loss']
73val_loss = history.history['val_loss']
74
75epochs = range(len(acc))
76
77plt.plot(epochs, acc, 'bo', label='Training acc')
78plt.plot(epochs, val_acc, 'b', label='Validation acc')
79plt.title('Training and validation accuracy')
80plt.legend()
81
82plt.figure()
83
84plt.plot(epochs, loss, 'bo', label='Training loss')
85plt.plot(epochs, val_loss, 'b', label='Validation loss')
86plt.title('Training and validation loss')
87plt.legend()
88
89plt.show()
90