Deep Learning with R Cookbook

How to do it...

Now, we can build our first sequential keras model and train it:

1. Let's start by defining a sequential model:

model_sequential <- keras_model_sequential()

2. We need to add layers to the model we defined in the preceding code block:

model_sequential %>% layer_dense(units = 16,batch_size = ,input_shape = c(784)) %>% layer_activation('relu') %>% layer_dense(units = 1)

3. After adding the layers to our model, we need to compile it:

model_sequential %>% compile( loss = "mse", optimizer = optimizer_sgd(), metrics = list("mean_absolute_error"))

4. Now, let's visualize the summary of the model we created:

model_sequential %>% summary()

The summary of the model is as follows:

5. Now, let's train the model and store the training stats ...