CNNで画像を3つのクラスに分類するモデルを作成したい

前提・実現したいこと

CNNで3つのクラスに分類するモデルを作成したいが、モデルをfitさせる際にエラーが
発生し計算することができない。

発生している問題・エラーメッセージ

InvalidArgumentError:  Received a label value of 3 which is outside the valid range of [0, 3).  Label values: 3 3 3 1 3 3 3 1 3 1 3 1 3 3 1 1 2 3 3 3 3 3 3 2 2 2 3 1 3 3 3 2
	 [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-27-ef4f701c1d6f>:1) ]] [Op:__inference_train_function_2140]

Function call stack:
train_function

該当のソースコード

python
1import tensorflow
2from tensorflow import keras
3import matplotlib.pyplot as plt
4%matplotlib inline
5import pandas as pd
6import numpy as np
7import glob
8import cv2
9from PIL import Image
10
11df2 = pd.read_csv("data2.csv")
12files = glob.glob("/Users/a440/Desktop/happy_images/*")
13df_files = pd.DataFrame({"data":files})
14
15df2["data"] = "/Users/a440/Desktop/happy_images/" + df2["data"] + ".jpg"
16df2 = df2.drop("Unnamed: 0", axis=1)
17df2.to_csv("data2-2.csv")
18df_join = pd.merge(df2, df_files, how="inner",on="data",indicator=True)
19
20#targetにsmileのカテゴリを代入
21smiles =df_join["smile"]
22target = []
23
24for smile in smiles:
25    data = np.asarray(smile)
26    target.append(data)
27y = np.array(target)
28
29#photo_array に画像データの配列を代入
30titles = df_join["data"]
31
32photo_array = []
33
34for title in titles:
35    bgr = Image.open(title)
36
37    bgr = bgr.convert("L")
38    bgr = bgr.resize((50, 50))
39    bgr = np.asarray(bgr)
40    bgr = bgr.reshape(50,50,1)
41    bgr = bgr/255
42    photo_array.append(bgr)
43
44x = np.array(photo_array)
45
46from sklearn.metrics import accuracy_score
47from sklearn.metrics import confusion_matrix
48from sklearn.model_selection import train_test_split
49(x_train,x_test, y_train, y_test) = train_test_split(x, y, test_size=0.3, random_state=0)
50
51num_class = 3
52
53model = keras.models.Sequential([
54    keras.layers.Conv2D(32, kernel_size=(3,3),     
55                                           activation="relu",
56                        input_shape=(50,50,1)),
57    keras.layers.MaxPooling2D(pool_size=(2,2)),
58    keras.layers.Conv2D(64, (3, 3), activation="relu"),
59    keras.layers.MaxPooling2D(pool_size=(2,2)),
60    keras.layers.Dropout(0.5),
61    keras.layers.Flatten(),
62    keras.layers.Dense(128, activation="relu"),
63    keras.layers.Dropout(0.25),
64    keras.layers.Dense(num_class, activation="softmax")
65                       
66])
67
68model.compile(optimizer="adam", loss="sparse_categorical_crossentropy",metrics=["accuracy"])
69
70history = model.fit(x_train, y_train, epochs=10)

OUT
1---------------------------------------------------------------------------
2InvalidArgumentError                      Traceback (most recent call last)
3<ipython-input-27-ef4f701c1d6f> in <module>
4----> 1 history = model.fit(x_train, y_train, epochs=10)
5
6/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
7   1098                 _r=1):
8   1099               callbacks.on_train_batch_begin(step)
9-> 1100               tmp_logs = self.train_function(iterator)
10   1101               if data_handler.should_sync:
11   1102                 context.async_wait()
12
13/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
14    826     tracing_count = self.experimental_get_tracing_count()
15    827     with trace.Trace(self._name) as tm:
16--> 828       result = self._call(*args, **kwds)
17    829       compiler = "xla" if self._experimental_compile else "nonXla"
18    830       new_tracing_count = self.experimental_get_tracing_count()
19
20/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
21    886         # Lifting succeeded, so variables are initialized and we can run the
22    887         # stateless function.
23--> 888         return self._stateless_fn(*args, **kwds)
24    889     else:
25    890       _, _, _, filtered_flat_args = \
26
27/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
28   2941        filtered_flat_args) = self._maybe_define_function(args, kwargs)
29   2942     return graph_function._call_flat(
30-> 2943         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
31   2944 
32   2945   @property
33
34/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
35   1917       # No tape is watching; skip to running the function.
36   1918       return self._build_call_outputs(self._inference_function.call(
37-> 1919           ctx, args, cancellation_manager=cancellation_manager))
38   1920     forward_backward = self._select_forward_and_backward_functions(
39   1921         args,
40
41/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
42    558               inputs=args,
43    559               attrs=attrs,
44--> 560               ctx=ctx)
45    561         else:
46    562           outputs = execute.execute_with_cancellation(
47
48/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
49     58     ctx.ensure_initialized()
50     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
51---> 60                                         inputs, attrs, num_outputs)
52     61   except core._NotOkStatusException as e:
53     62     if name is not None:
54
55InvalidArgumentError:  Received a label value of 3 which is outside the valid range of [0, 3).  Label values: 3 3 3 1 3 3 3 1 3 1 3 1 3 3 1 1 2 3 3 3 3 3 3 2 2 2 3 1 3 3 3 2
56	 [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-27-ef4f701c1d6f>:1) ]] [Op:__inference_train_function_2140]
57
58Function call stack:
59train_function