質問編集履歴
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コードが足りなかったため
test
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test
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```
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File "learning2.py", line 161, in <module>
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shuffle=True,callbacks=[early_stopping])
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File "/home/mlab-user/anaconda3/envs/image/lib/python3.5/site-packages/keras/engine/training.py", line 970, in fit
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batch_size=batch_size)
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File "/home/mlab-user/anaconda3/envs/image/lib/python3.5/site-packages/keras/engine/training.py", line 787, in _standardize_user_data
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exception_prefix='target')
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File "/home/mlab-user/anaconda3/envs/image/lib/python3.5/site-packages/keras/engine/training_utils.py", line 127, in standardize_input_data
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'with shape ' + str(data_shape))
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ValueError: Error when checking target: expected dense_2 to have 2 dimensions, but got array with shape (10800, 3, 3, 3)
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```ここに言語名を入力
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import keras
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from keras.utils import np_utils
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from keras.models import Sequential
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from keras.layers.convolutional import MaxPooling2D
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from keras.layers import Conv2D, Flatten, Dense, Dropout
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from sklearn.model_selection import train_test_split
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from PIL import Image
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from keras.callbacks import EarlyStopping
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import numpy as np
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import glob
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import matplotlib.pyplot as plt
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from sklearn.model_selection import KFold
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import confusion_matrix
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classes = ["犬","猫","兎"
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]
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num_classes = len(classes)
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X = []
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Y = []
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for index, classlabel in enumerate(classes):
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dir = "./" + classlabel
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files = glob.glob(dir + "/*.jpg")
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for i, file in enumerate(files):
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image = Image.open(file)
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image = image.convert("RGB")
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image = image.resize((128, 128))
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data = np.asarray(image)
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X.append(data)
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Y.append(index)
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X = np.array(X)
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Y = np.array(Y)
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X = X.astype('float32')/255.0
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Y = np_utils.to_categorical(Y, num_classes)
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model.add(Conv2D(128, (3, 3), padding='same', activation="relu"))
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model.add(Conv2D(128, (3, 3), padding='same', activation="relu"))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128 ,activation="relu"))
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hist = model.fit(train_data, train_label, batch_size=1
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hist = model.fit(train_data, train_label, batch_size=10, verbose=1,
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epochs=10, validation_data=(val_data, y_test),
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model.save('./cnn.h5')
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```
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