写一串基于卷积神经网络的回归预测代码
时间: 2023-03-31 18:01:21 浏览: 99
以下是基于卷积神经网络的回归预测代码示例:
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten
# 生成随机数据
X = np.random.rand(100, 10, 10, 3)
y = np.random.rand(100, 1)
# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(10, 10, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(1, activation='linear'))
# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(X, y, epochs=10, batch_size=10)
# 使用模型进行预测
X_test = np.random.rand(10, 10, 10, 3)
y_pred = model.predict(X_test)
```
希望这个代码示例能够帮助你!
相关问题
写一串卷积神经网络代码
以下是一个简单的卷积神经网络的代码示例:
```python
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10)
])
model.summary()
```
这个模型包含了三个卷积层和两个全连接层,用于对 MNIST 手写数字数据集进行分类。
如何写基于lstm神经网络实现对一串数列进行回归预测的python代码
以下是基于LSTM神经网络实现对一串数列进行回归预测的Python代码:
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
# 设置训练数据
X = np.array([[1,2,3],[2,3,4],[3,4,5],[4,5,6]])
y = np.array([4,5,6,7])
# 设置模型
model = Sequential()
model.add(LSTM(units=1, input_shape=(3,1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
X = X.reshape((X.shape[0], X.shape[1], 1))
model.fit(X, y, epochs=100, batch_size=1, verbose=2)
# 预测
x_input = np.array([5,6,7])
x_input = x_input.reshape((1,3,1))
yhat = model.predict(x_input, verbose=0)
print(yhat)