data = data_all.reshape(np.prod(data_all.shape[:2]), np.prod(data_all.shape[2:]))
时间: 2024-01-18 07:05:14 浏览: 30
This line of code reshapes a 4-dimensional numpy array called "data_all" into a 2-dimensional numpy array called "data" by flattening the first two dimensions and combining them into one dimension, and flattening the last two dimensions and combining them into another dimension.
The function np.prod() calculates the product of all elements of an array. In this case, it is used to calculate the total number of elements in the first two dimensions of "data_all".
The resulting "data" array will have the same number of elements as "data_all", but arranged in a different shape. This type of reshaping is commonly used in machine learning and data analysis tasks.
相关问题
cgan_model.h5怎么生成
生成 cgan_model.h5 的步骤如下:
1. 准备数据集,包括真实图片和对应的标签。
2. 定义生成器和判别器网络结构。生成器用于生成假图片,判别器用于判断真假图片。
3. 定义损失函数和优化器。损失函数可以是二元交叉熵或 Wasserstein 损失等。优化器可以选择 Adam 优化器或其他优化器。
4. 训练模型。使用真实图片和标签训练判别器,使用生成器生成假图片并与标签一起训练判别器。最后,将生成器和判别器合并为一个 cgan_model 模型。
5. 保存 cgan_model 模型为 cgan_model.h5。
下面是一个简单的 Keras 代码示例:
```python
from keras.models import Model
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, Embedding, multiply
from keras.layers import Conv2DTranspose, Conv2D, Lambda
from keras.optimizers import Adam
from keras.datasets import mnist
import keras.backend as K
# 定义生成器
def build_generator(z_dim, img_shape, num_classes):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, z_dim)(label))
x = multiply([z, emb])
x = Dense(256, activation='relu')(x)
x = Reshape((1, 1, 256))(x)
x = Conv2DTranspose(128, 4, strides=1, padding='valid')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(1, 4, strides=2, padding='same')(x)
img = Activation('tanh')(x)
return Model([z, label], img)
# 定义判别器
def build_discriminator(img_shape, num_classes):
img = Input(shape=img_shape)
label = Input(shape=(1,), dtype='int32')
emb = Flatten()(Embedding(num_classes, np.prod(img_shape))(label))
emb = Reshape(img_shape)(emb)
x = multiply([img, emb])
x = Conv2D(64, 4, strides=2, padding='same')(x)
x = Activation('relu')(x)
x = Conv2D(128, 4, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid')(x)
return Model([img, label], x)
# 定义损失函数和优化器
def wasserstein_loss(y_true, y_pred):
return K.mean(y_true * y_pred)
def build_cgan(generator, discriminator):
z = Input(shape=(z_dim,))
label = Input(shape=(1,), dtype='int32')
img = generator([z, label])
discriminator.trainable = False
validity = discriminator([img, label])
return Model([z, label], validity)
generator = build_generator(z_dim, img_shape, num_classes)
discriminator = build_discriminator(img_shape, num_classes)
cgan = build_cgan(generator, discriminator)
optimizer = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999)
discriminator.compile(loss=wasserstein_loss, optimizer=optimizer, metrics=['accuracy'])
cgan.compile(loss=wasserstein_loss, optimizer=optimizer)
# 训练模型
for epoch in range(num_epochs):
for i, (imgs, labels) in enumerate(data_loader):
z = np.random.normal(0, 1, (batch_size, z_dim))
gen_imgs = generator.predict([z, labels])
d_loss_real = discriminator.train_on_batch([imgs, labels], -np.ones((batch_size, 1)))
d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], np.ones((batch_size, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
z = np.random.normal(0, 1, (batch_size, z_dim))
labels = np.random.randint(0, num_classes, batch_size).reshape(-1, 1)
g_loss = cgan.train_on_batch([z, labels], -np.ones((batch_size, 1)))
# 保存模型
if epoch % save_interval == 0:
generator.save_weights('cgan_model.h5')
```
在训练完成后,可以使用以下代码将生成器的权重保存为 cgan_model.h5 文件:
```python
generator.save_weights('cgan_model.h5')
```
编写基于TensorFlow和LSTM模型的Python自定义类ModelLstm预测全球风速代码,读取720个tif文件,每个文件表示一小时时间间隔,每个tif包含像元数[721,1440],表示全球格网点,作为训练数据,用3小时数据预测未来3小时风速
好的,让我来帮你编写这个ModelLstm类。
首先,需要导入一些必要的库和模块:
```python
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import numpy as np
import os
from osgeo import gdal
```
然后,我们可以定义ModelLstm类,包括初始化函数和训练函数:
```python
class ModelLstm:
def __init__(self, input_shape=(721, 1440), output_shape=(721, 1440), lstm_units=128, dense_units=128, epochs=50, batch_size=16):
self.input_shape = input_shape
self.output_shape = output_shape
self.lstm_units = lstm_units
self.dense_units = dense_units
self.epochs = epochs
self.batch_size = batch_size
self.model = self.build_model()
def build_model(self):
model = Sequential()
model.add(LSTM(self.lstm_units, input_shape=self.input_shape, return_sequences=True))
model.add(Dense(self.dense_units))
model.add(Dense(np.prod(self.output_shape), activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
def train(self, data_path, model_path):
x, y = self.load_data(data_path)
checkpoint = ModelCheckpoint(model_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
early_stop = EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='min')
callbacks_list = [checkpoint, early_stop]
self.model.fit(x, y, epochs=self.epochs, batch_size=self.batch_size, validation_split=0.1, callbacks=callbacks_list)
def load_data(self, data_path):
data_list = os.listdir(data_path)
data_list.sort()
x = []
y = []
for i in range(len(data_list)-6):
data_x = []
for j in range(6):
data = gdal.Open(data_path + '/' + data_list[i+j])
data_band = data.GetRasterBand(1)
data_array = data_band.ReadAsArray()
data_x.append(data_array)
x.append(data_x)
data_y = gdal.Open(data_path + '/' + data_list[i+6])
data_band = data_y.GetRasterBand(1)
data_array = data_band.ReadAsArray()
y.append(data_array)
x = np.array(x)
y = np.array(y)
x = x.reshape(x.shape[0], x.shape[1], x.shape[2], x.shape[3], 1)
y = y.reshape(y.shape[0], y.shape[1]*y.shape[2])
return x, y
```
在这个ModelLstm类中,我们定义了初始化函数,包括输入形状、输出形状、LSTM单元数、全连接层单元数、迭代次数和批次大小。我们还定义了build_model函数,用于建立LSTM模型,并定义了train函数,用于训练模型。
在load_data函数中,我们首先获取数据文件夹中的所有文件,并根据文件名排序。然后,我们读取6个小时的数据作为输入,读取第7个小时的数据作为输出,并将它们分别存入x和y数组中。最后,我们将x和y数组转换为numpy数组,并将x数组的形状调整为(batch_size, 6, 721, 1440, 1),将y数组的形状调整为(batch_size, 721*1440)。
现在,我们可以使用ModelLstm类来训练模型了。假设我们有一个名为data的数据文件夹,并且我们想要将训练好的模型保存到名为model.h5的文件中,我们可以这样做:
```python
model_lstm = ModelLstm()
model_lstm.train('data', 'model.h5')
```
这将会使用data文件夹中的数据来训练模型,并将训练好的模型保存到model.h5文件中。