画像テストでのエラー

CNNを用いた機械学習を行なっております。機械学習データ（hdf5ファイル）は作成できたのですが、そのファイルを用いた認識テストを行おうとしたらエラーがかかりました。アドバイスお願いします。

python
1import cnn_model
2import keras
3import matplotlib.pyplot as plt
4import numpy as np
5from PIL import Image
6import matplotlib.pyplot as plt
7import urllib.request
8
9im_rows = 32 # 画像の縦ピクセルサイズ
10im_cols = 32 # 画像の横ピクセルサイズ
11im_color = 3 # 画像の色空間
12in_shape = (im_rows, im_cols, im_color)
13nb_classes = 4
14
15LABELS = ["瀬戸大橋","サンポート高松","屋島","直島"]
16
17# 保存したCNNモデルを読み込む
18model = cnn_model.get_model(in_shape, nb_classes)
19model.load_weights('./image/photo.hdf5')
20
21def check_photo(path):
22    # 画像を読み込む
23    img = Image.open(path)
24    img = img.convert("RGB") # 色空間をRGBに
25    img = img.resize((im_cols, im_rows)) # サイズ変更
26    plt.imshow(img)
27    plt.show()
28    # データに変換
29    x = np.asarray(img)
30    x = x.reshape(-1, im_rows, im_cols, im_color)
31    x = x / 255
32
33    # 予測
34    pre = model.predict([x])[0]
35    idx = pre.argmax()
36    per = int(pre[idx] * 100)
37    return (idx, per)
38
39def check_photo_str(path):
40    idx, per = check_photo(path)
41    # 答えを表示
42    print("この写真は、", LABELS[idx])
43    print("可能性は、", per, "%")
44
45
46
47
48if __name__ == '__main__':
49    check_photo_str('test1.jpg')
50    check_photo_str('test2.jpg')
51    check_photo_str('test3.jpg')
52    check_photo_str('test4.jpg')
53    check_photo_str('test5.jpg')

python
1エラーコード
2　　runfile('/Users/nakayakenta/my_photo2.py')
3Reloaded modules: cnn_model
4Traceback (most recent call last):
5
6  File "<ipython-input-5-3614c52dbdd8>", line 1, in <module>
7    runfile('/Users/nakayakenta/my_photo2.py')
8
9  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 668, in runfile
10    execfile(filename, namespace)
11
12  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/spyder_kernels/customize/spydercustomize.py", line 108, in execfile
13    exec(compile(f.read(), filename, 'exec'), namespace)
14
15  File "/Users/nakayakenta/my_photo2.py", line 27, in <module>
16    model.load_weights('./image/photo.hdf5')
17
18  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/keras/engine/network.py", line 1166, in load_weights
19    f, self.layers, reshape=reshape)
20
21  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/keras/engine/saving.py", line 1058, in load_weights_from_hdf5_group
22    K.batch_set_value(weight_value_tuples)
23
24  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2465, in batch_set_value
25    assign_op = x.assign(assign_placeholder)
26
27  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 1718, in assign
28    name=name)
29
30  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 221, in assign
31    validate_shape=validate_shape)
32
33  File "/Users/nakayakenta/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 61, in assign
34    use_locking=use_locking, name=name)
35
36  File "/Users/name/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
37    op_def=op_def)
38
39  File "/Users/name/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
40    return func(*args, **kwargs)
41
42  File "/Users/name/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
43    op_def=op_def)
44
45  File "/Users/name/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1792, in __init__
46    control_input_ops)
47
48  File "/Users/name/anaconda3/envs/python35/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1631, in _create_c_op
49    raise ValueError(str(e))
50
51ValueError: Dimension 1 in both shapes must be equal, but are 4 and 28. Shapes are [512,4] and [512,28]. for 'Assign_32' (op: 'Assign') with input shapes: [512,4], [512,28].