Tensorflowによるモデル学習でtensorflow.python.framework.errors

以下のコードを実行した所、モデルの学習の所で、
tensorflow.python.framework.errors_impl.InvalidArgumentError
が発生してしまいます。
原因と対処方法をご教示頂けると幸いです。

Python
1import sys , os
2import random
3
4import numpy as np
5import cv2
6
7
8# 画像をランダムの位置で切り抜くプログラム
9def random_clip(img , clip_size , num = 10):
10    clip_images = []
11    height, width = img.shape[:2]
12
13    # 画像をclip_sizeサイズごとにnum回切り抜く
14    for y in range( num ):
15        rand_y = random.randint(0,height - clip_size)
16        rand_x = random.randint(0,width - clip_size)        
17        clip_img = img[ rand_y : rand_y + clip_size, rand_x : rand_x + clip_size]
18        clip_img = clip_img.flatten().astype(np.float32)/255.0
19        clip_images.append(clip_img)
20
21    return clip_images
22
23
24# データセットから画像とラベルをランダムに取得
25def random_sampling( images , labels , train_num , test_num = 0  ):
26    image_train_batch = []
27    label_train_batch = []
28
29    #乱数を発生させ，リストを並び替える．
30    random_seq = list(range(len(images)))
31    random.shuffle(random_seq)
32
33    # バッチサイズ分画像を選択
34    image_train_batch = images[ :train_num ]
35    label_train_batch = labels[ :train_num ]
36
37    if test_num == 0: # 検証用データの指定がないとき
38        return image_train_batch , label_train_batch
39    else:
40        image_test_batch = images[ train_num : train_num + test_num ]
41        label_test_batch = labels[ train_num : train_num + test_num ]
42        return image_train_batch , label_train_batch , image_test_batch , label_test_batch
43
44
45# フォルダーの画像をランダム位置でクリップした後にリサイズして読み込む
46def make( folder_name , img_size = 0 , clip_num = 0 , clip_size = 0 ,train_num = 0 , test_num = 0 ):
47    train_image = []
48    test_image = []
49    train_label = []
50    test_label= []
51
52    # フォルダ内のディレクトリの読み込み
53    classes = os.listdir( folder_name )
54
55    for i, d in enumerate(classes):
56        files = os.listdir( folder_name + '/' + d  )
57
58        tmp_image = []
59        tmp_label = []
60        for f in files:
61            # 1枚の画像に対する処理
62            if not 'jpg' in f:# jpg以外のファイルは無視
63                continue
64
65            # 画像読み込み
66            img = cv2.imread( folder_name+ '/' + d + '/' + f)
67            # one_hot_vectorを作りラベルとして追加
68            label = np.zeros(len(classes))
69            if d == "0":
70               label[i] = 0
71            elif d == "1":
72               label[i] = 1
73            else:
74               label[i] = 0
75
76            # リサイズをする処理
77            if img_size != 0:
78                img = cv2.resize( img , (img_size , img_size ))
79                img = img.flatten().astype(np.float32)/255.0
80                tmp_image.append(img)                
81                tmp_label.append(label)
82            elif clip_size != 0 and clip_num != 0:
83                img = random_clip( img , clip_size , clip_num)
84                tmp_image.extend( img )                    
85                for j in range(clip_num):
86                    tmp_label.append(label)
87            else:
88                img = img.flatten().astype(np.float32)/255.0
89                tmp_image.append(img)
90                tmp_label.append(label)
91
92
93        # データセットのサイズが指定されているときはその枚数分ランダムに抽出する
94        if train_num == 0 :
95            train_image.extend( tmp_image )
96            train_label.extend( tmp_label )
97        #　テスト画像の指定がないとき
98        elif test_num == 0 :
99            sampled_image , sampled_label = random_sampling( tmp_image , tmp_label , train_num )
100            train_image.extend( sampled_image )
101            train_label.extend( sampled_label )
102        else :
103            sampled_train_image , sampled_train_label , sampled_test_image , sampled_test_label = random_sampling( tmp_image , tmp_label , train_num , test_num )
104            train_image.extend( sampled_train_image )
105            train_label.extend( sampled_train_label )
106            test_image.extend( sampled_test_image )
107            test_label.extend( sampled_test_label )
108
109        print(d + 'read complete ,' + str(len(train_label)) + ' pictures exit')
110
111    # numpy配列に変換
112    train_image = np.asarray( train_image )
113    train_label = np.asarray( train_label )
114
115    if test_num != 0: #testデータセットがあるときは返り値が変わる
116        test_image = np.asarray( test_image )
117        test_label = np.asarray( test_label )
118        return train_image , train_label , test_image , test_label
119
120    return train_image , train_label

Python
1# TensorFlow と tf.keras のインポート
2import tensorflow as tf
3from tensorflow import keras
4
5# ヘルパーライブラリのインポート
6import numpy as np
7import matplotlib.pyplot as plt
8
9print(tf.__version__)
10
11#mnist = keras.datasets.mnist
12
13#(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
14import image_dataset
15train_images ,train_labels, test_images ,test_labels = image_dataset.make('C:/Users/dataset',img_size = 32, train_num=10000 , test_num =1000)
16
17train_images.shape
18#print(train_images.shape, train_labels.shape)の結果
19print(train_images.shape, train_labels.shape)
20
21#plt.figure()
22#plt.imshow(train_images[0], cmap='gray')
23#plt.colorbar()
24#plt.grid(False)
25#試しに表示
26#plt.show()
27
28len(train_labels)
29train_labels
30test_images.shape
31len(test_labels)
32
33#データの前処理
34#plt.figure()
35#plt.imshow(train_images[0])
36#plt.colorbar()
37#plt.grid(False)
38#plt.show()
39
40train_images = train_images / 255.0
41test_images = test_images / 255.0
42
43model = keras.Sequential([
44    keras.layers.Flatten(input_shape=(32, 32, 3)),
45    keras.layers.Dense(128, activation='relu'),
46    keras.layers.Dense(10, activation='softmax')
47])
48#モデルのコンパイル
49model.compile(optimizer='adam', 
50              loss='sparse_categorical_crossentropy',
51              metrics=['accuracy'])
52#モデルの訓練
53model.fit(train_images, train_labels, epochs=5)
54
55# save model
56model.save("model_cnn.h5")
57
58test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
59#正解率の評価
60print('\nTest accuracy:', test_acc)
61
62print("end")

実行結果

2021-02-17 11:33:13.182244: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not f
ound
2021-02-17 11:33:13.192100: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2.4.1
0read complete ,1 pictures exit
1read complete ,2 pictures exit

(10, 3072) (10, 10)
2021-02-17 11:33:18.164654: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
～中略～
2021-02-17 11:33:18.459982: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
Epoch 1/5
WARNING:tensorflow:Model was constructed with shape (None, 32, 32, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name='flatten_inpu
t'), name='flatten_input', description="created by layer 'flatten_input'"), but it was called on an input with incompatible shape (None, 3072).
WARNING:tensorflow:Model was constructed with shape (None, 32, 32, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name='flatten_inpu
t'), name='flatten_input', description="created by layer 'flatten_input'"), but it was called on an input with incompatible shape (None, 3072).
Traceback (most recent call last):
  File "cnn_image.py", line 53, in <module>
    model.fit(train_images, train_labels, epochs=5)
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1100, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\def_function.py", line 888, in _call
    return self._stateless_fn(*args, **kwds)
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\function.py", line 2942, in __call__
    return graph_function._call_flat(
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\function.py", line 1918, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\function.py", line 555, in call
    outputs = execute.execute(
  File "C:\Users\anaconda3\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  logits and labels must have the same first dimension, got logits shape [10,10] and labels shape [100]
         [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at cnn_image.py:53) ]] [Op:__inference_train_
function_536]

Function call stack:
train_function

Tensorflow2.4.1
Python3.7
データセットに使用している画像はRGBカラー画像です。
model.summary()の結果

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
flatten_layer (Flatten)      (None, 2352)              0
_________________________________________________________________
dense (Dense)                (None, 128)               301184
_________________________________________________________________
dropout (Dropout)            (None, 128)               0
_________________________________________________________________
dense_1 (Dense)              (None, 10)                1290
=================================================================
Total params: 302,474
Trainable params: 302,474
Non-trainable params: 0