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25
4
我正在训练一个简单的对抗性图像来破坏预训练的模型。但是，我在 fit() 过程中获得的结果与对同一输入(恒定输入)调用 Predict() 不同。
model.trainable = False
gan = Sequential()
gan.add(Dense( 256 * 256 * 3, use_bias=False, input_shape=(1,)))
gan.add(Reshape((256, 256, 3)))
gan.add(model)
gan.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 196608) 196608
_________________________________________________________________
reshape_2 (Reshape) (None, 256, 256, 3) 0
_________________________________________________________________
sequential_1 (Sequential) (None, 2) 24952610
=================================================================
Total params: 25,149,218
Trainable params: 196,608
Non-trainable params: 24,952,610
_________________________________________________________________
img = img.reshape(256, 256, 3)
def custom_loss(layer):
# Create a loss function that adds the MSE loss to the mean of all squared activations of a specific layer
def loss(y_true,y_pred):
y_true = K.print_tensor(y_true, message='y_true = ')
y_pred = K.print_tensor(y_pred, message='y_pred = ')
label_diff = K.square(y_pred - y_true)
return K.mean(label_diff)
# Return a function
return loss
gan.compile(optimizer='adam',
loss=custom_loss(gan.layers[1]), # Call the loss function with the selected layer
metrics=['accuracy'])
x = np.ones((1,1))
goal = np.array([0, 1])
y = goal.reshape((1,2))
gan.fit(x, y, epochs=300, verbose=1)
在fit()期间，损失明显减少
Epoch 1/300
1/1 [==============================] - 5s 5s/step - loss: 0.9950 - acc: 0.0000e+00
...
Epoch 300/300
1/1 [==============================] - 0s 46ms/step - loss: 0.0045 - acc: 1.0000
在后端，y_pred和y_true也是正确的
......
y_true = [[0 1]]
y_pred = [[0.100334756 0.899665236]]
y_true = [[0 1]]
y_pred = [[0.116679631 0.883320332]]
y_true = [[0 1]]
y_pred = [[0.0832592845 0.916740656]]
y_true = [[0 1]]
y_pred = [[0.098835744 0.901164234]]
y_true = [[0 1]]
y_pred = [[0.0979194269 0.902080595]]
y_true = [[0 1]]
y_pred = [[0.057831794 0.942168236]]
y_true = [[0 1]]y_pred = [[0.0760448873 0.923955142]]
y_true = [[0 1]]
y_pred = [[0.041532293 0.958467722]]
y_true = [[0 1]]
y_pred = [[0.0667938739 0.933206141]]
print(gan.predict(x))
给予
[[0.99923825 0.00076174]]
尝试使用预训练的 Resnet 和 InceptionV3，两者都遇到了相同的问题。附上model.summary()
对于启动:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
inception_v3 (Model) (None, None, None, 2048) 21802784
_________________________________________________________________
global_average_pooling2d_1 ( (None, 2048) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 2098176
_________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 1049600
_________________________________________________________________
dropout_2 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_3 (Dense) (None, 2) 2050
=================================================================
Total params: 24,952,610
Trainable params: 14,264,706
Non-trainable params: 10,687,904
_________________________________________________________________
对于 Resnet:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 262, 262, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 128, 128, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 128, 128, 64) 256 conv1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 128, 128, 64) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, 130, 130, 64) 0 activation_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 64, 64, 64) 0 pool1_pad[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, 64, 64, 64) 4160 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 64, 64, 64) 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 64, 64, 64) 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, 64, 64, 64) 36928 activation_2[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 64, 64, 64) 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 64, 64, 64) 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, 64, 64, 256) 16640 activation_3[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, 64, 64, 256) 16640 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 64, 64, 256) 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 64, 64, 256) 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 64, 64, 256) 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 64, 64, 256) 0 add_1[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, 64, 64, 64) 16448 activation_4[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 64, 64, 64) 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 64, 64, 64) 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, 64, 64, 64) 36928 activation_5[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 64, 64, 64) 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 64, 64, 64) 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, 64, 64, 256) 16640 activation_6[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 64, 64, 256) 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 64, 64, 256) 0 bn2b_branch2c[0][0]
activation_4[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 64, 64, 256) 0 add_2[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, 64, 64, 64) 16448 activation_7[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 64, 64, 64) 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 64, 64, 64) 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, 64, 64, 64) 36928 activation_8[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 64, 64, 64) 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 64, 64, 64) 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, 64, 64, 256) 16640 activation_9[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 64, 64, 256) 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 64, 64, 256) 0 bn2c_branch2c[0][0]
activation_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 64, 64, 256) 0 add_3[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, 32, 32, 128) 32896 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 32, 32, 128) 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_11[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 32, 32, 128) 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_12[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, 32, 32, 512) 131584 activation_10[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 32, 32, 512) 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 32, 32, 512) 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 32, 32, 512) 0 add_4[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_13[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 32, 32, 128) 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_14[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 32, 32, 128) 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_15[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 32, 32, 512) 0 bn3b_branch2c[0][0]
activation_13[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 32, 32, 512) 0 add_5[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_16[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 32, 32, 128) 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_17[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 32, 32, 128) 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_18[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 32, 32, 512) 0 bn3c_branch2c[0][0]
activation_16[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 32, 32, 512) 0 add_6[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, 32, 32, 128) 65664 activation_19[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, 32, 32, 128) 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 32, 32, 128) 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, 32, 32, 128) 147584 activation_20[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, 32, 32, 128) 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 32, 32, 128) 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, 32, 32, 512) 66048 activation_21[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, 32, 32, 512) 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 32, 32, 512) 0 bn3d_branch2c[0][0]
activation_19[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 32, 32, 512) 0 add_7[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, 16, 16, 256) 131328 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 16, 16, 256) 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_23[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 16, 16, 256) 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_24[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, 16, 16, 1024) 525312 activation_22[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 16, 16, 1024) 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 16, 16, 1024) 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 16, 16, 1024) 0 add_8[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_25[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 16, 16, 256) 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_26[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 16, 16, 256) 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_27[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
add_9 (Add) (None, 16, 16, 1024) 0 bn4b_branch2c[0][0]
activation_25[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 16, 16, 1024) 0 add_9[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_28[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 16, 16, 256) 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 16, 16, 256) 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, 16, 16, 256) 590080 activation_29[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 16, 16, 256) 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 16, 16, 256) 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, 16, 16, 1024) 263168 activation_30[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 16, 16, 1024) 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
add_10 (Add) (None, 16, 16, 1024) 0 bn4c_branch2c[0][0]
activation_28[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 16, 16, 1024) 0 add_10[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, 16, 16, 256) 262400 activation_31[0][0]
__________________________________________________________________________________________________
... omitted ...
Total params: 23,593,859
Trainable params: 23,540,739
Non-trainable params: 53,120
__________________________________________________________________________________________________
最佳答案
这些预训练模型包含 BatchNormalization 层。
预计它们在训练和测试之间的表现会有所不同(对于 Dropout 层也是如此，但差异不会那么大)。
训练期间的BatchNormalization将使用当前批处理的均值和方差进行标准化，它还会针对一个批处理可能无法代表完整数据集的事实应用一些统计补偿。
但在评估过程中，BatchNormalization 将使用在训练期间收集的调整后的值来计算平均值和变化。 (在这种情况下，是在“预训练”期间收集的，而不是您的训练期间收集的)
为了使 BatchNormalization 正常工作，您需要预训练模型的输入与模型的原始训练数据处于同一范围内。否则，您必须让 BatchNormalization 层保持可训练状态，以便根据您的数据调整均值和方差。
但是您的训练需要大量的批量大小以及真实数据才能正确训练。
<小时/>训练图像的提示。
在导入预训练模型的同一模块中，您可以导入 preprocess_input 函数。给它一些使用 keras.preprocessing.images.load_img 加载的图像，看看模型的预期范围是多少。
使用ImageDataGenerator时，您可以传递此preprocess_input函数，以便生成器为您提供预期的数据。
关于tensorflow - Keras 模型在 fit() 和 Predict() 期间对相同输入给出不同的预测，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58605766/
25
4
0
我有兴趣在 tf.keras 中训练一个模型，然后用 keras 加载它。我知道这不是高度建议，但我对使用 tf.keras 来训练模型很感兴趣，因为 tf.keras 更容易构建输入管道 我想利用
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我正在阅读一篇名为 Differential Learning Rates 的文章在 Medium 上，想知道这是否可以应用于 Keras。我能够找到在 pytorch 中实现的这项技术。这可以在 K
我正在实现一个神经网络分类器，以打印我正在使用的这个神经网络的损失和准确性: score = model.evaluate(x_test, y_test, verbose=False) model.m
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我正在尝试使用 Keras 创建自定义损失函数。我想根据输入计算损失函数并预测神经网络的输出。 我尝试在 Keras 中使用 customloss 函数。我认为 y_true 是我们为训练提供的输出，
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DL 中的一项常见任务是将输入样本归一化为零均值和单位方差。可以使用如下代码“手动”执行规范化: mean = np.mean(X, axis = 0) std = np.std(X, axis =
我正在尝试学习 Keras 并使用 LSTM 解决分类问题。我希望能够绘制 准确率和损失，并在训练期间更新图。为此，我正在使用 callback function . 由于某种原因，我在回调中收到的准
在 Keras 内置函数中嵌入使用哪种算法？Word2vec？手套？其他？ https://keras.io/layers/embeddings/ 最佳答案 简短的回答是都不是。本质上，GloVe 的
我有一个使用 Keras 完全实现的 LSTM RNN，我想使用梯度剪裁，梯度范数限制为 5(我正在尝试复制一篇研究论文)。在实现神经网络方面，我是一个初学者，我将如何实现？ 是否只是(我正在使用 r
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