回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>我对keras和tensorflow还比较陌生。我试图弄明白为什么在使用稠密层=2而非稠密层=1时运行代码会出现错误。这是我如何根据dir结构分配类的:</p>
<pre><code>res_scans = np.array([process_scan(path) for path in res_scan_paths])
non_res_scans = np.array([process_scan(path) for path in non_res_scan_paths])
# For the CT scans having presence of viral pneumonia
# assign 1, for the normal ones assign 0.
res_labels = np.array([1 for _ in range(len(res_scans))])
non_res_labels = np.array([0 for _ in range(len(non_res_scans))])
#Split data
spl = 0.7
resF = round(spl * len(res_scan_paths) )
non_resF = round(spl * len(non_res_scan_paths))
# Split data in the ratio 70-30 for training and validation.
x_train = np.concatenate((res_scans[:resF], non_res_scans[:non_resF]), axis=0)
y_train = np.concatenate((res_labels[:resF], non_res_labels[:non_resF]), axis=0)
x_test = np.concatenate((res_scans[resF:], non_res_scans[non_resF:]), axis=0)
y_test = np.concatenate((res_labels[resF:], non_res_labels[non_resF:]), axis=0)
</code></pre>
<p>这是CNN模型,使用VGG16在conv层之间进行批量标准化</p>
<pre><code>#VGG16 Model
lr = 0.001
activ='relu'
Model = Sequential()
Model.add(Conv3D(64, (3,3,3), strides=(1, 1, 1), input_shape = (91, 109, 91, 30), activation =activ, padding="same"))
Model.add(Conv3D(64, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(MaxPool3D((2,2,2), strides = (2,2,2)))
Model.add(Conv3D(128, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(128, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(MaxPool3D((2,2,2), strides = (1,1,1)))
Model.add(Conv3D(256, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(256, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(256, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(MaxPool3D((2,2,2), strides = (2,2,2)))
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(MaxPool3D((2,2,2), strides = (2,2,2)))
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(Conv3D(512, (3,3,3), strides=(1, 1, 1), activation =activ, padding="same"))
Model.add(BatchNormalization())
Model.add(MaxPool3D((2,2,2), strides = (1,1,1)))
Model.add(Flatten())
Model.add(Dense(units=4096,activation="relu"))
Model.add(Dense(units=4096,activation="relu"))
Model.add(Dense(units=2, activation="softmax"))
Model.summary()
</code></pre>
<p>然后在下面编译并运行它</p>
<pre><code>Model.compile(loss='mse' , optimizer=tf.keras.optimizers.Adam(lr=lr), metrics=["acc"])
history = Model.fit(x_train, y_train, epochs=30, batch_size=12, verbose=1,validation_data=(x_test, y_test))
</code></pre>
<p>我有一个让我困惑的错误:</p>
<pre><code>ValueError: A target array with shape (18, 1) was passed for output of shape (None, 2) while using as loss `mean_squared_error`. This loss expects targets to have the same shape as the output.
</code></pre>
<p>我可以在另一台只使用CPU的计算机上很好地运行它。然而,在装有2桥2080ti的计算机上,它给了我这个错误。当我把它改为1时,它在第一个纪元之后给了我死内核。
我从以前的帖子中知道,密度=2是正确的方法。请帮忙</p>
<p>凯文</p>