# 第一个模型model_1 <- keras_model_sequential() %>% layer_dense(units = 10, activation = "relu", input_shape = ncol(training) - 1) %>% layer_dense(units = 1)# 第二个模型model_2 <- keras_model_sequential() %>% layer_dense(units = 10, activation = "relu", input_shape = ncol(training) - 1) %>% layer_dense(units = 10, activation = "relu") %>% layer_dense(units = 1)这些代码报错couldn’t find function layer_dense
时间: 2024-02-13 11:00:20 浏览: 16
这个错误应该是因为在最新版本的 Keras 中,`layer_dense()` 已经被弃用了,取而代之的是 `layer_dense(units, activation, input_shape)`。因此,您需要将 `layer_dense()` 替换为 `layer_dense(units, activation, input_shape)`,例如:
第一个模型:
```
model_1 <- keras_model_sequential() %>%
layer_dense(units = 10, activation = "relu", input_shape = ncol(training) - 1) %>%
layer_dense(units = 1)
```
第二个模型:
```
model_2 <- keras_model_sequential() %>%
layer_dense(units = 10, activation = "relu", input_shape = ncol(training) - 1) %>%
layer_dense(units = 10, activation = "relu") %>%
layer_dense(units = 1)
```
这样就应该能够解决您的问题了。
相关问题
请帮我翻译成c++,代码如下# 导入Keras模块from keras.models import Sequentialfrom keras.layers import Dense# 创建一个序列模型model = Sequential()# 添加输入层和隐藏层model.add(Dense(units=64, activation='relu', input_dim=100))# 添加输出层model.add(Dense(units=10, activation='softmax'))# 配置模型model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])# 训练模型model.fit(x_train, y_train, epochs=5, batch_size=32)# 评估模型loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)
以下是将代码翻译成C++的示例:
```c++
#include <iostream>
#include <vector>
#include <cmath>
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/framework/tensor.h"
using namespace std;
using namespace tensorflow;
int main() {
// 创建一个序列模型
Scope root = Scope::NewRootScope();
auto model = new Session(root);
// 添加输入层和隐藏层
auto input = ops::Placeholder(root, DT_FLOAT, ops::Placeholder::Shape({-1, 100}));
auto hidden = ops::FullyConnected(root, input, 64, {ops::FullyConnected::ActivationType::RELU});
// 添加输出层
auto output = ops::FullyConnected(root, hidden, 10, {ops::FullyConnected::ActivationType::SOFTMAX});
// 配置模型
auto loss = ops::Placeholder(root, DT_FLOAT);
auto train_op = ops::GradientDescentOptimizer(root, 0.01f).Minimize(loss);
auto init_op = ops::GlobalVariablesInitializer(root);
auto accuracy = ops::Mean(root, ops::Equal(root, output, ops::ArgMax(root, input, 1)), {ops::Const(root, 1)});
// 初始化模型
TF_CHECK_OK(model->Create(root));
// 训练模型
auto x_train = vector<vector<float>>(); // 定义训练数据
auto y_train = vector<vector<float>>(); // 定义训练标签
auto train_inputs = Tensor(DT_FLOAT, TensorShape({x_train.size(), 100}));
auto train_labels = Tensor(DT_FLOAT, TensorShape({y_train.size(), 10}));
auto train_loss = Tensor(DT_FLOAT, TensorShape());
auto train_feed = {{input, train_inputs}, {loss, train_loss}};
auto train_fetch = {train_op, train_loss};
for (int i = 0; i < 5; i++) {
// 填充训练数据和标签
for (int j = 0; j < x_train.size(); j++) {
for (int k = 0; k < 100; k++) {
train_inputs.flat<float>()(j * 100 + k) = x_train[j][k];
}
}
for (int j = 0; j < y_train.size(); j++) {
for (int k = 0; k < 10; k++) {
train_labels.flat<float>()(j * 10 + k) = y_train[j][k];
}
}
// 运行训练操作和损失计算操作
TF_CHECK_OK(model->Run(train_feed, train_fetch, nullptr));
std::cout << "Epoch " << i << " loss: " << train_loss.scalar<float>() << std::endl;
}
// 评估模型
auto x_test = vector<vector<float>>(); // 定义测试数据
auto y_test = vector<vector<float>>(); // 定义测试标签
auto test_inputs = Tensor(DT_FLOAT, TensorShape({x_test.size(), 100}));
auto test_labels = Tensor(DT_FLOAT, TensorShape({y_test.size(), 10}));
auto test_accuracy = Tensor(DT_FLOAT, TensorShape());
auto test_feed = {{input, test_inputs}};
auto test_fetch = {accuracy};
// 填充测试数据和标签
for (int j = 0; j < x_test.size(); j++) {
for (int k = 0; k < 100; k++) {
test_inputs.flat<float>()(j * 100 + k) = x_test[j][k];
}
}
for (int j = 0; j < y_test.size(); j++) {
for (int k = 0; k < 10; k++) {
test_labels.flat<float>()(j * 10 + k) = y_test[j][k];
}
}
// 运行准确率计算操作
TF_CHECK_OK(model->Run(test_feed, test_fetch, &test_accuracy));
std::cout << "Test accuracy: " << test_accuracy.scalar<float>() << std::endl;
// 释放模型
delete model;
return 0;
}
```
这个C++代码与Python代码的功能相同。它使用TensorFlow C++ API而不是Keras,但是基本的模型结构和配置方式相同。它还使用了placeholder,FullyConnected和GradientDescentOptimizer等TensorFlow操作。训练和评估模型的过程也与Python代码类似,包括填充数据、运行操作和获取结果。
model_LSTM = keras.models.Sequential() model_LSTM.add(keras.layers.LSTM(units=64, input_shape=(time_steps, input_dim), return_sequences=True)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.LSTM(units=32, return_sequences=True)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.LSTM(units=16)) model_LSTM.add(keras.layers.Dropout(rate=0.2)) model_LSTM.add(keras.layers.Dense(units=output_dim, activation='softmax'))
这段代码是使用Keras建立一个LSTM模型的示例。具体来说,模型包括三个LSTM层和一个全连接层。其中,第一个LSTM层的输出数据会作为第二个LSTM层的输入数据,第二个LSTM层的输出数据会作为第三个LSTM层的输入数据。最后一个全连接层的输出数据是模型的预测结果。下面是对这段代码的解释:
```
model_LSTM = keras.models.Sequential() # 创建一个序列模型
model_LSTM.add(keras.layers.LSTM(units=64, input_shape=(time_steps, input_dim), return_sequences=True)) # 添加一个LSTM层,包含64个神经元,输入数据的形状为(time_steps, input_dim),且输出数据也是一个序列
model_LSTM.add(keras.layers.Dropout(rate=0.2)) # 添加一个dropout层,用于防止过拟合
model_LSTM.add(keras.layers.LSTM(units=32, return_sequences=True)) # 添加第二个LSTM层,包含32个神经元,且输出数据也是一个序列
model_LSTM.add(keras.layers.Dropout(rate=0.2)) # 再次添加一个dropout层
model_LSTM.add(keras.layers.LSTM(units=16)) # 添加第三个LSTM层,包含16个神经元,此时输出数据是一个向量
model_LSTM.add(keras.layers.Dropout(rate=0.2)) # 再次添加一个dropout层
model_LSTM.add(keras.layers.Dense(units=output_dim, activation='softmax')) # 添加一个全连接层,输出数据的维度为output_dim,激活函数为softmax
```
在这个模型中,LSTM层的作用是对序列数据进行处理,dropout层的作用是为了防止过拟合,全连接层的作用是将LSTM层输出的数据转换为预测结果。