Now that you have imported the necessary libraries and loaded your data, it's time to create your neural network. In Keras, models are defined as a sequence of layers. We will create a simple neural network with two hidden layers.
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
# Add the first hidden layer
model.add(Dense(128, activation='relu', input_shape=(784,)))
# Add the second hidden layer
model.add(Dense(64, activation='relu'))
# Add the output layer
model.add(Dense(10, activation='softmax'))
The first layer is the input layer. It has 784 neurons, which is the same number of features as our data. The activation function for this layer is `relu`, which is a non-linear function that helps the network learn complex relationships between the features.
The second layer is a hidden layer. It has 64 neurons. The activation function for this layer is also `relu`.
The output layer has 10 neurons, one for each class in our dataset. The activation function for this layer is `softmax`, which ensures that the output of the network is a probability distribution.
Once we have defined our model, we need to compile it. This involves specifying the loss function, the optimizer, and the metrics that we want to track.
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
The loss function is used to measure the error between the predicted outputs of the network and the actual outputs. The optimizer is used to update the weights of the network during training. The metrics are used to evaluate the performance of the network.
Now that our model is compiled, we can train it. We will use the `fit` method to train the network on our data.
model.fit(x_train, y_train, epochs=10)
The `fit` method takes three arguments: the training data, the training labels, and the number of epochs. The number of epochs is the number of times that the network will be trained on the data.
After the network has been trained, we can evaluate its performance on the test data.
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
The `evaluate` method returns two values: the loss and the accuracy. The loss is a measure of the error between the predicted outputs of the network and the actual outputs. The accuracy is a measure of how often the network correctly classifies the data.
In this example, the test loss is 0.06 and the test accuracy is 99.0%. This means that the network is able to correctly classify 99% of the data in the test set.
This is just a simple example of how to create a neural network in Keras in Tensorflow. With a little more practice, you will be able to create more complex networks that can solve a wider range of problems.
