I introduction by crespo
Crespo is an open source deep learning framework based on Python, which can run on the back end of Tensorflow, Theano and CNTK. Crespo's design goal is to achieve rapid experiments and allow users to quickly build, train and deploy deep learning models. The advantages of Crespo are simplicity, modular design and expansibility. Its API design is simple and intuitive, and users can get started quickly.
Second, the installation of Crespo.
Before using Crespo, we need to install it. We can install Crespo through pip, as follows:
```
Pippi stahle X.
```
Third, the use of Crespo.
1. Import Crespo module
Before using Crespo, we need to import Crespo module first. The specific code is as follows:
```
Imported goods
```
Build a model
In Crespo, we can build a model through sequential classes. Sequential classes allow us to add neural network layers in sequence. The specific code is as follows:
```
fromkeras . modelsimportsequential
Fromkeras.layersimportDense, activating
Model = Order ()
model.add(Dense(32，input_dim=784))
Model.add (activate ('relu'))
Model.add (Dense (10))
Model.add (activate ('softmax'))
```
In this example, we construct a simple fully connected neural network, which has an input layer, a hidden layer and an output layer. The input layer dimension is 784, the hidden layer dimension is 32, and the output layer dimension is 10. We used ReLU activation function and Softmax activation function.
3. Compile the model
After building the model, we need to compile it. When compiling the model, we need to specify the loss function, optimizer and evaluation index. The specific code is as follows:
```
model . compile(loss = ' categorial _ cross entropy '，
Optimizer ='adam',
Metric = ['Accuracy'])
```
In this example, we use cross entropy as the loss function, Adam as the optimizer and accuracy as the evaluation index.
4. Training mode
After compiling the model, we need to train the model. When training the model, we need to specify training data, training rounds and batches. The specific code is as follows:
```
model.fit(x_train，y_train，
Era = 10,
Batch size =32)
```
In this example, the number of rounds of our training model is 10, and the batch size is 32.
5. Evaluation model
After training the model, we need to evaluate its performance. When evaluating the model, we need to specify the test data. The specific code is as follows:
```
Loss, accuracy = model. Evaluation (x _ test, y _ test)
```
In this example, we evaluate the loss and accuracy of the model.
Fourth, the advantages of Crespo.
1. Easy to use
Crespo's API design is simple and intuitive, and users can get started quickly. Users can easily build, train and deploy deep learning models.
2. Standard design
Crespo's modular design allows users to easily add, delete and modify the neural network layer. Users can design neural network models according to their own needs.
3. Measurability
Crespo can run in Tensorflow, Theano, CNTK and other backend, and users can choose the backend according to their own needs. At the same time, Crespo also supports distributed training, which allows users to train deep learning models more efficiently.