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我想通過Keras(theano後端)中的一些練習來了解CNNs。我無法適應下面的模型(錯誤:AttributeError:'Convolution2D'對象沒有'get_shape'屬性)。該數據集是來自MNIST數據的圖像(28 * 28),最多連接5個圖像。所以輸入形狀應該是1,28,140(灰度= 1,高度= 28,寬度= 28 * 5)Keras用於多位數識別
目標是預測數字序列。謝謝!!
batch_size = 128
nb_classes = 10
nb_epoch = 2
img_rows =28
img_cols=140
img_channels = 1
model_input=(img_channels, img_rows, img_cols)
x = Convolution2D(32, 3, 3, border_mode='same')(model_input)
x = Activation('relu')(x)
x = Convolution2D(32, 3, 3)(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
conv_out = Flatten()(x)
x1 = Dense(nb_classes, activation='softmax')(conv_out)
x2 = Dense(nb_classes, activation='softmax')(conv_out)
x3 = Dense(nb_classes, activation='softmax')(conv_out)
x4 = Dense(nb_classes, activation='softmax')(conv_out)
x5 = Dense(nb_classes, activation='softmax')(conv_out)
lst = [x1, x2, x3, x4, x5]
model = Sequential(input=model_input, output=lst)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(dataset, data_labels, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1)
感謝您的回覆。我仍然覺得張量對象是不可迭代的。 –
是'model.fit()'的錯誤。如果是的話,我的猜測是'data_labels'應該是長度爲5的numpy數組列表。每個numpy數組應該是維數'dataset.shape [0] x nb_classes' – indraforyou
嗨,錯誤發生在激活層。以下是完整代碼的鏈接:https://gist.github.com/jdills26/ca69e59ef19d4993636f6b50a7cbe514感謝您的幫助!這裏是數據源:http://yann.lecun.com/exdb/mnist/index.html –