Keras CNN中级没有任何功能更改

Question

我发现我的数据集的中间层完全没有任何特征变化，如下所示：

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其中，Google ML Crash Course中的例子(猫和狗)显示了更丰富的不同特征，如下所示：

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我相信正是因为这个原因，CNN在我的数据集上的准确率低得离谱，只有6%左右……

我想知道有没有人知道为什么？

我不认为这是我的数据集的问题(14个类，每个类的amount_of_images = 100-450，训练集中总共有~2500张图像)，所以我将跳过这篇文章中关于数据集的细节。我认为这更多的是一个与CNN有关的问题...？

我将我的CNN架构设计如下：

# Our input feature map is 150x150x3: 150x150 for the image pixels, and 3 for
# the three color channels: R, G, and B
img_input = layers.Input(shape=(150, 150, 3))
print(img_input)

# First convolution extracts 16 filters that are 3x3
# Convolution is followed by max-pooling layer with a 2x2 window
x = layers.Conv2D(16, 3, activation='relu', strides=(1,1), padding='same')(img_input)
# x = layers.MaxPooling2D(2)(x)
print(x)

# Second convolution extracts 32 filters that are 3x3
# Convolution is followed by max-pooling layer with a 2x2 window
x = layers.Conv2D(32, 3, activation='relu',strides=(2,2), padding='same')(x)
# x = layers.MaxPooling2D(2)(x)
print(x)

# Third convolution extracts 64 filters that are 3x3
# Convolution is followed by max-pooling layer with a 2x2 window
x = layers.Conv2D(64, 3, activation='relu', strides=(2,2), padding='same')(x)
# x = layers.MaxPooling2D(2)(x)
print(x)

# forth convolution extracts 128 filters that are 3x3
# Convolution is followed by max-pooling layer with a 2x2 window
x = layers.Conv2D(128, 3, activation='relu', strides=(2,2), padding='same')(x)
# x = layers.MaxPooling2D(2)(x)
print(x)

#fifth convolution extracts 256 filters that are 3x3
#Convolution is followed by max-pooling layer with a 2x2 window
x = layers.Conv2D(256, 3, activation='relu', strides=(2,2), padding='same')(x)
# x = layers.MaxPooling2D(2)(x)
print(x)

它可以被看作是以下内容：

Tensor("input_1:0", shape=(?, 150, 150, 3), dtype=float32)
Tensor("conv2d/Relu:0", shape=(?, 150, 150, 16), dtype=float32)
Tensor("conv2d_1/Relu:0", shape=(?, 75, 75, 32), dtype=float32)
Tensor("conv2d_2/Relu:0", shape=(?, 38, 38, 64), dtype=float32)
Tensor("conv2d_3/Relu:0", shape=(?, 19, 19, 128), dtype=float32)
Tensor("conv2d_4/Relu:0", shape=(?, 10, 10, 256), dtype=float32)

然后

# Flatten feature map to a 1-dim tensor so we can add fully connected layers
x = layers.Flatten()(x)

# Create a fully connected layer with ReLU activation and 512 hidden units
x = layers.Dense(512, activation='relu')(x)

# Create output layer with a single node and sigmoid activation
output = layers.Dense(1, activation='sigmoid')(x)

# Create model:
# input = input feature map
# output = input feature map + stacked convolution/maxpooling layers + fully 
# connected layer + sigmoid output layer
model = Model(img_input, output)

以下是每个时期的结果：

Epoch 1/15
126/126 - 92s - loss: -4.6284e+13 - acc: 0.0647 - val_loss: -3.0235e+14 - val_acc: 0.0615
Epoch 2/15
126/126 - 50s - loss: -3.6250e+15 - acc: 0.0639 - val_loss: -1.2503e+16 - val_acc: 0.0615
Epoch 3/15
126/126 - 50s - loss: -4.7133e+16 - acc: 0.0639 - val_loss: -1.2015e+17 - val_acc: 0.0615
Epoch 4/15
126/126 - 50s - loss: -3.0991e+17 - acc: 0.0639 - val_loss: -6.4998e+17 - val_acc: 0.0615
Epoch 5/15
126/126 - 51s - loss: -1.3102e+18 - acc: 0.0639 - val_loss: -2.4530e+18 - val_acc: 0.0615
Epoch 6/15
126/126 - 50s - loss: -4.2291e+18 - acc: 0.0639 - val_loss: -7.3530e+18 - val_acc: 0.0615
Epoch 7/15
126/126 - 50s - loss: -1.1655e+19 - acc: 0.0639 - val_loss: -1.8978e+19 - val_acc: 0.0615
Epoch 8/15
126/126 - 50s - loss: -2.8185e+19 - acc: 0.0639 - val_loss: -4.3459e+19 - val_acc: 0.0615
Epoch 9/15
126/126 - 50s - loss: -6.0396e+19 - acc: 0.0639 - val_loss: -9.0798e+19 - val_acc: 0.0615
Epoch 10/15
126/126 - 51s - loss: -1.2250e+20 - acc: 0.0639 - val_loss: -1.7633e+20 - val_acc: 0.0615
Epoch 11/15
126/126 - 49s - loss: -2.2829e+20 - acc: 0.0639 - val_loss: -3.2223e+20 - val_acc: 0.0615
Epoch 12/15
126/126 - 50s - loss: -4.0790e+20 - acc: 0.0639 - val_loss: -5.5966e+20 - val_acc: 0.0615
Epoch 13/15
126/126 - 51s - loss: -6.9094e+20 - acc: 0.0639 - val_loss: -9.3551e+20 - val_acc: 0.0615
Epoch 14/15
126/126 - 50s - loss: -1.1305e+21 - acc: 0.0639 - val_loss: -1.5039e+21 - val_acc: 0.0615
Epoch 15/15
126/126 - 50s - loss: -1.7871e+21 - acc: 0.0639 - val_loss: -2.3466e+21 - val_acc: 0.0615

为了损失和准确性，我还在这里附上了两个图表：

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它基本上是一个Google ML Crash Course的模型，我只是在这里和那里更改了参数，并应用我自己的数据来看看会发生什么。

我的理论是，由于未知的原因，CNN没有从我的数据集中提取任何特征，因此网络在两个时期内就找到了局部最小值。

当我对这个问题摸不着头脑时，任何帮助都将不胜感激。

Answer 1

如果你有14个类，这是没有意义的：

# Create output layer with a single node and sigmoid activation
output = layers.Dense(1, activation='sigmoid')(x)

你应该对14个神经元使用softmax激活：

output = layers.Dense(14, activation='softmax')(x)

然后，您应该确保使用正确的损失(分类或稀疏交叉熵)，并在损失需要时对标签进行一次热编码。