我在准备数据集以提供1D CNN时遇到问题。
我的CSV有3025个列，表示单个字节+最后一个列作为字符串标签。
也许这不是预处理的问题，而是我的网络模型的问题。
这是我的型号：
def cnn_1d(num_classes):
model = models.Sequential()
model.add(Conv1D(16, kernel_size=3, strides=1, activation="relu", input_shape=(3025, 1)))
model.add(Conv1D(16, kernel_size=3, strides=1, activation="relu"))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv1D(32, kernel_size=3, strides=1, activation="relu"))
model.add(Conv1D(32, kernel_size=3, strides=1, activation="relu"))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.2))
model.add(Dense(500, activation="relu"))
model.add(Dense(300, activation="relu"))
model.add(Dense(num_classes, activation="softmax"))
model.compile(
optimizer=keras.optimizers.Adam(1e-3),
loss="categorical_crossentropy",
metrics=["accuracy"],
)
model.summary()
return model
这就是我试图预处理数据的方法：
num_classes = 4
df = pd.read_csv("test.csv", header=0)
df["label"] = pd.Categorical(df["label"])
df["label"] = df.label.cat.codes
Y = df.pop("label")
X = df.copy()
x_train, x_test, y_train, y_test = train_test_split(np.asarray(X), np.asarray(Y), test_size=0.33, shuffle=True)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
model = cnn_1d(num_classes)
model.fit(x_train, y_train, epochs=100, batch_size=64, validation_data=(x_test, y_test))
我想我在最后一个密集层上得到了一个错误，因为标签预处理不正确。这个i
ValueError: Shapes (None, 1) and (None, 753, 4) are incompatible
我缺少什么？我所知道的是，最后一个密集层应该有num个类作为单位计数(在我的例子中是4(。
这是您上面展示的代码的模型摘要：
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d (Conv1D) (None, 3023, 16) 64
_________________________________________________________________
conv1d_1 (Conv1D) (None, 3021, 16) 784
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 1510, 16) 0
_________________________________________________________________
dropout (Dropout) (None, 1510, 16) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 1508, 32) 1568
_________________________________________________________________
conv1d_3 (Conv1D) (None, 1506, 32) 3104
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 753, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 753, 32) 0
_________________________________________________________________
dense (Dense) (None, 753, 500) 16500
_________________________________________________________________
dense_1 (Dense) (None, 753, 300) 150300
_________________________________________________________________
dense_2 (Dense) (None, 753, 4) 1204
=================================================================
Total params: 173,524
Trainable params: 173,524
Non-trainable params: 0
输出层的维度为(批、序列长度、4个类(。您可能是想在第二个最大冷却层之后使矩阵变平。
如果我这样做，我会得到一个参数较少的模型，它将输出4个类中的一个。。。
def cnn_1d(num_classes):
model = models.Sequential()
model.add(Conv1D(16, kernel_size=3, strides=1, activation="relu", input_shape=(3025, 1)))
model.add(Conv1D(16, kernel_size=3, strides=1, activation="relu"))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv1D(32, kernel_size=3, strides=1, activation="relu"))
model.add(Conv1D(32, kernel_size=3, strides=1, activation="relu"))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(500, activation="relu"))
model.add(Dense(300, activation="relu"))
model.add(Dense(num_classes, activation="softmax"))
model.compile(
optimizer=keras.optimizers.Adam(1e-3),
loss="categorical_crossentropy",
metrics=["accuracy"],
)
model.summary()
return model
cnn_1d(4)
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d_4 (Conv1D) (None, 3023, 16) 64
_________________________________________________________________
conv1d_5 (Conv1D) (None, 3021, 16) 784
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 1510, 16) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 1510, 16) 0
_________________________________________________________________
conv1d_6 (Conv1D) (None, 1508, 32) 1568
_________________________________________________________________
conv1d_7 (Conv1D) (None, 1506, 32) 3104
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 753, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 24096) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 24096) 0
_________________________________________________________________
dense_3 (Dense) (None, 500) 12048500
_________________________________________________________________
dense_4 (Dense) (None, 300) 150300
_________________________________________________________________
dense_5 (Dense) (None, 4) 1204
=================================================================
Total params: 12,205,524
Trainable params: 12,205,524
Non-trainable params: 0
此外，该模型的可训练参数明显较少。
我发现了我缺少的东西：
- 我错过了在y_*变量上使用to_categorical。我认为它已经和
df["label"] = pd.Categorical(df["label"])分类了。所以在模型之前，我添加了：y_train = to_categorical(y_train, 4) y_test = to_categorical(y_test, 4) - 我忘记了在最后一个MaxPool1D层之后压平输出
现在它可以正常工作了。