如何在Keras模型中添加卷积层？

Question

[包含A，B，C的数据。应从A和B值预测C值。

数据被分割和缩放。

没有卷积层的模型。

from keras.models import Sequential
from keras.layers import Dense, Conv1D

features_count = len(X_train.columns)

model = Sequential([
    Dense(64, input_shape=(features_count,)),
    Activation('relu'),
    Dense(32),
    Activation('softmax'),
    Dense(1),
])

model.compile(optimizer="adam", loss='mse')
model.fit(X_train.values, y_train.values, epochs=10, batch_size=1, verbose=1)
model.evaluate(X_test, y_test, batch_size=1)

结果：1.0033315420150757

添加了Conv1D：

model = Sequential([
    Conv1D(filters=256, kernel_size=5, padding='same', activation='relu', input_shape=(features_count,)),
    Dense(64),
    Activation('relu'),
    Dense(32),
    Activation('softmax'),
    Dense(1),
])

结果：输入0与层conv1d_3不兼容：预期ndim = 3，找到的ndim = 2

input_shape=(features_count,)

被替换为

input_shape=(features_count,1)

结果：检查输入时出错：预期conv1d_4_input具有3个维，但数组的形状为（3，2）]

以这种方式添加卷积层有什么问题？如何在Keras模型中添加卷积层？

Answer 1

Conv1D需要2D数据，因为它可以沿第二维对第一维应用卷积。因此，您的输入数据必须具有形状（批大小1、2）。但是，密集层需要1D，因此在Conv1D和密集之间需要添加一个Flatten层以1D形式转换数据。

from keras.models import Sequential
from keras.layers import Dense, Conv1D, Activation, Flatten
import numpy as np
X_train = np.array([[-1.0,-1.0], [1.0, -1.0], [1.0,1.0]])
y_train = np.array([[1.0], [-1.0], [0.0]])

X_test = np.array([[1.0,-1.0], [1.0, -1.0]])
y_test = np.array([[1.0], [-1.0]])

features_count = 2

model = Sequential([
    Conv1D(filters=256, kernel_size=5, padding='same', activation='relu', input_shape=(1,features_count)),
    Flatten(),
    Dense(64),
    Activation('relu'),
    Dense(32),
    Activation('softmax'),
    Dense(1),
])

X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])

model.compile(optimizer="adam", loss='mse')
model.fit(X_train, y_train, epochs=10, batch_size=1, verbose=1)
model.evaluate(X_test, y_test, batch_size=1)

结果： 1.00140380859375

希望对您有帮助。