以下是我的模型创建代码:
import tensorflow as tf from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Activation model = tf.keras.models.Sequential() model.add(Conv2D(40, kernel_size=5, padding="same",input_shape=(300, 300, 1), activation = 'relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Conv2D(70, kernel_size=3, padding="same", activation = 'relu')) model.add(Conv2D(500, kernel_size=3, padding="same", activation = 'relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Conv2D(1024, kernel_size=3, padding="valid", activation = 'relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dense(units=100, activation='relu' )) model.add(Dropout(0.8)) model.add(Dense(2)) model.add(Activation("softmax")) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) optim = "adam" model.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy'])train = train_data[:858]test = train_data[859:]X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)Y = [i[1] for i in train]Y=np.array(Y)print(X.shape)(858, 300, 300, 1)model.fit(X, Y, epochs=1)
以下是我的错误信息:任何帮助都将不胜感激。
谢谢另外,我的图像大小是300 —————————————————————————
ValueError Traceback (most recent call last)<ipython-input-131-d4fc87229b94> in <module>()----> 1 model.fit(X, Y, epochs=1)3 frames/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_utils.py in check_loss_and_target_compatibility(targets, loss_fns, output_shapes) 739 raise ValueError('A target array with shape ' + str(y.shape) + 740 ' was passed for an output of shape ' + str(shape) +--> 741 ' while using as loss `' + loss_name + '`. ' 742 'This loss expects targets to have the same shape ' 743 'as the output.')ValueError: A target array with shape (858, 2) was passed for an output of shape (None, 36, 36, 2) while using as loss `categorical_crossentropy`. This loss expects targets to have the same shape as the output
我的图像大小是300
回答:
你忘记在这一行之前添加Flatten()
层:
model.add(Dense(units=100, activation='relu'))
所以只需添加:
model.add(Flatten())