CNNでエラーが起きる

Question

pythonでCNNを実装しようとしたところ、以下のエラーが起きました。

犬と猫の画像分類ではうまくいってるのですが、分類を3つに増やしたら動かなくなりました

from sklearn.model_selection import train_test_split
from PIL import Image
import glob
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten,Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
import matplotlib.pyplot as plt


folder = ["group1", "group2", "group3"]
image_size = 100

X = []
Y = []
for index, name in enumerate(folder):
    dir = "./" + name
    files = glob.glob(dir + "/*.jpg")
    for i, file in enumerate(files):
        image = Image.open(file)
        image = image.convert("RGB")
        image = image.resize((image_size, image_size))
        data = np.asarray(image)
        X.append(data)
        Y.append(index)

X = np.array(X)
Y = np.array(Y)


X = X.astype('float32')
X = X / 255.0


# 正解ラベルの形式を変換
Y = np_utils.to_categorical(Y, 3)


# 学習用データとテストデータ
X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size=0.15)
x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.1)

# CNNを構築
model = Sequential()

model.add(Conv2D(filters=96, kernel_size=(11, 11), padding='same', input_shape=X_train.shape[1:], activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(11, 11), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=385, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=385, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=385, kernel_size=(3, 3), activation='relu'))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(4096))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(2))
model.add(Activation('softmax'))

model.summary()

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

epochs = 2
batch_size = 2

history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_val, y_val))

Error
ValueError: Error when checking target: expected activation_3 to have shape (2,) but got array with shape (3,)

データの形が悪いのはわかるのですが、解決できないです

Answer 1

outputが2になってました。