outputs = self.model.predict(img.reshape(1, 224, 224, 3), verbose=1)
时间: 2023-10-11 08:13:55 浏览: 95
这段代码使用了一个模型来对一个图像进行预测。具体来说,它将一个形状为(224, 224, 3)的图像重塑为一个形状为(1, 224, 224, 3)的张量,并将其输入到名为"model"的模型中进行预测。预测的结果存储在变量"outputs"中,并且通过设置verbose参数为1来打印出模型的预测过程。需要注意的是,这段代码的执行过程涉及到了神经网络的计算,因此需要使用GPU等加速设备来提高计算速度。
相关问题
将冒号后面的代码改写成一个nn.module类:data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])
以下是将代码改写为nn.module类的示例:
```python
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
class LSTMModel(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super(LSTMModel, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
out = self.fc(out[:, -1, :])
return out
# 读取数据
data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030')
df = data1.fillna(method='ffill')
data = df.values.reshape(-1, 1)
# 数据归一化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
# 划分数据集
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]
# 创建数据集
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 30
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))
# 模型训练
input_dim = 1
hidden_dim = 50
output_dim = 1
num_layers = 2
model = LSTMModel(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 6
for epoch in range(num_epochs):
outputs = model(trainX)
optimizer.zero_grad()
loss = criterion(outputs, trainY)
loss.backward()
optimizer.step()
if epoch % 1 == 0:
print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))
# 预测结果
trainPredict = model(trainX)
testPredict = model(testX)
trainPredict = scaler.inverse_transform(trainPredict.detach().numpy())
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict.detach().numpy())
testY = scaler.inverse_transform([testY])
```
将冒号后面的代码改写成一个nn.module类:import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])
以下是将代码改写成一个nn.module类:
```python
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, look_back):
super().__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.look_back = look_back
self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
x = x.view(-1, self.look_back, 1)
out, _ = self.lstm1(x, (h0, c0))
out, _ = self.lstm2(out, (h0, c0))
out = self.fc(out[:, -1, :])
return out
data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030')
df = data1.fillna(method='ffill')
data = df.values.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size, :], data[train_size:len(data), :]
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 30
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTMModel(input_size=1, hidden_size=50, output_size=1, num_layers=2, look_back=look_back).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 6
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(zip(trainX, trainY)):
inputs = torch.from_numpy(inputs).float().to(device)
labels = torch.from_numpy(labels).float().to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(trainX)}], Loss: {loss.item():.4f}')
model.eval()
trainPredict = scaler.inverse_transform(model(torch.from_numpy(trainX).float().to(device)).detach().cpu().numpy())
testPredict = scaler.inverse_transform(model(torch.from_numpy(testX).float().to(device)).detach().cpu().numpy())
trainY = scaler.inverse_transform([trainY])
testY = scaler.inverse_transform([testY])
```
这个类接受5个参数,分别是:
- input_size: LSTM的输入维度,对于这个例子来说,input_size=1
- hidden_size: LSTM的隐藏层维度
- output_size: LSTM的输出维度,对于这个例子来说,output_size=1
- num_layers: LSTM的层数
- look_back: 每个样本的序列长度,对于这个例子来说,look_back=30