The sequential model is a linear stack of layers.

You create a sequential model by calling the `keras_model_sequential()`

function then a series of `layer`

functions:

```
library(keras)
<- keras_model_sequential()
model %>%
model layer_dense(units = 32, input_shape = c(784)) %>%
layer_activation('relu') %>%
layer_dense(units = 10) %>%
layer_activation('softmax')
```

Note that Keras objects are modified in place which is why it’s not necessary for `model`

to be assigned back to after the layers are added.

Print a summary of the model’s structure using the `summary()`

function:

`summary(model)`

```
Model
________________________________________________________________________________
Layer (type) Output Shape Param #
================================================================================
dense_1 (Dense) (None, 256) 200960
________________________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
________________________________________________________________________________
dense_2 (Dense) (None, 128) 32896
________________________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
________________________________________________________________________________
dense_3 (Dense) (None, 10) 1290
================================================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
________________________________________________________________________________
```

The model needs to know what input shape it should expect. For this reason, the first layer in a sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape.

As illustrated in the example above, this is done by passing an `input_shape`

argument to the first layer. This is a list of integers or `NULL`

entries, where `NULL`

indicates that any positive integer may be expected. In `input_shape`

, the batch dimension is not included.

If you ever need to specify a fixed batch size for your inputs (this is useful for stateful recurrent networks), you can pass a `batch_size`

argument to a layer. If you pass both `batch_size=32`

and `input_shape=c(6, 8)`

to a layer, it will then expect every batch of inputs to have the batch shape `(32, 6, 8)`

.

Before training a model, you need to configure the learning process, which is done via the `compile()`

function. It receives three arguments:

An optimizer. This could be the string identifier of an existing optimizer (e.g. as “rmsprop” or “adagrad”) or a call to an optimizer function (e.g.

`optimizer_sgd()`

).A loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function (e.g. “categorical_crossentropy” or “mse”) or a call to a loss function (e.g.

`loss_mean_squared_error()`

).A list of metrics. For any classification problem you will want to set this to

`metrics = c('accuracy')`

. A metric could be the string identifier of an existing metric or a call to metric function (e.g.`metric_binary_crossentropy()`

).

Here’s the definition of a model along with the compilation step (the `compile()`

function has arguments appropriate for a multi-class classification problem):

```
# For a multi-class classification problem
<- keras_model_sequential()
model %>%
model layer_dense(units = 32, input_shape = c(784)) %>%
layer_activation('relu') %>%
layer_dense(units = 10) %>%
layer_activation('softmax')
%>% compile(
model optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = c('accuracy')
)
```

Here’s what compilation might look like for a mean squared error regression problem:

```
%>% compile(
model optimizer = optimizer_rmsprop(lr = 0.002),
loss = 'mse'
)
```

Here’s compilation for a binary classification problem:

```
%>% compile(
model optimizer = optimizer_rmsprop(),
loss = loss_binary_crossentropy,
metrics = metric_binary_accuracy
)
```

Here’s compilation with a custom metric:

```
# create metric using backend tensor functions
<- custom_metric("mean_pred", function(y_true, y_pred) {
metric_mean_pred k_mean(y_pred)
})
%>% compile(
model optimizer = optimizer_rmsprop(),
loss = loss_binary_crossentropy,
metrics = c('accuracy', metric_mean_pred)
)
```

Keras models are trained on R matrices or higher dimensional arrays of input data and labels. For training a model, you will typically use the `fit()`

function.

Here’s a single-input model with 2 classes (binary classification):

```
# create model
<- keras_model_sequential()
model
# add layers and compile the model
%>%
model layer_dense(units = 32, activation = 'relu', input_shape = c(100)) %>%
layer_dense(units = 1, activation = 'sigmoid') %>%
compile(
optimizer = 'rmsprop',
loss = 'binary_crossentropy',
metrics = c('accuracy')
)
# Generate dummy data
<- matrix(runif(1000*100), nrow = 1000, ncol = 100)
data <- matrix(round(runif(1000, min = 0, max = 1)), nrow = 1000, ncol = 1)
labels
# Train the model, iterating on the data in batches of 32 samples
%>% fit(data, labels, epochs=10, batch_size=32) model
```

Here’s a single-input model with 10 classes (categorical classification):

```
# create model
<- keras_model_sequential()
model
# define and compile the model
%>%
model layer_dense(units = 32, activation = 'relu', input_shape = c(100)) %>%
layer_dense(units = 10, activation = 'softmax') %>%
compile(
optimizer = 'rmsprop',
loss = 'categorical_crossentropy',
metrics = c('accuracy')
)
# Generate dummy data
<- matrix(runif(1000*100), nrow = 1000, ncol = 100)
data <- matrix(round(runif(1000, min = 0, max = 9)), nrow = 1000, ncol = 1)
labels
# Convert labels to categorical one-hot encoding
<- to_categorical(labels, num_classes = 10)
one_hot_labels
# Train the model, iterating on the data in batches of 32 samples
%>% fit(data, one_hot_labels, epochs=10, batch_size=32) model
```

Here are a few examples to get you started!

On the examples page you will also find example models for real datasets:

Some additional examples are provided below.

```
library(keras)
# generate dummy data
<- matrix(runif(1000*20), nrow = 1000, ncol = 20)
x_train
<- runif(1000, min = 0, max = 9) %>%
y_train round() %>%
matrix(nrow = 1000, ncol = 1) %>%
to_categorical(num_classes = 10)
<- matrix(runif(100*20), nrow = 100, ncol = 20)
x_test
<- runif(100, min = 0, max = 9) %>%
y_test round() %>%
matrix(nrow = 100, ncol = 1) %>%
to_categorical(num_classes = 10)
# create model
<- keras_model_sequential()
model
# define and compile the model
%>%
model layer_dense(units = 64, activation = 'relu', input_shape = c(20)) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 10, activation = 'softmax') %>%
compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_sgd(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE),
metrics = c('accuracy')
)
# train
%>% fit(x_train, y_train, epochs = 20, batch_size = 128)
model
# evaluate
<- model %>% evaluate(x_test, y_test, batch_size = 128) score
```

```
library(keras)
# generate dummy data
<- matrix(runif(1000*20), nrow = 1000, ncol = 20)
x_train <- matrix(round(runif(1000, min = 0, max = 1)), nrow = 1000, ncol = 1)
y_train <- matrix(runif(100*20), nrow = 100, ncol = 20)
x_test <- matrix(round(runif(100, min = 0, max = 1)), nrow = 100, ncol = 1)
y_test
# create model
<- keras_model_sequential()
model
# define and compile the model
%>%
model layer_dense(units = 64, activation = 'relu', input_shape = c(20)) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 1, activation = 'sigmoid') %>%
compile(
loss = 'binary_crossentropy',
optimizer = 'rmsprop',
metrics = c('accuracy')
)
# train
%>% fit(x_train, y_train, epochs = 20, batch_size = 128)
model
# evaluate
= model %>% evaluate(x_test, y_test, batch_size=128) score
```

```
library(keras)
# generate dummy data
<- array(runif(100 * 100 * 100 * 3), dim = c(100, 100, 100, 3))
x_train
<- runif(100, min = 0, max = 9) %>%
y_train round() %>%
matrix(nrow = 100, ncol = 1) %>%
to_categorical(num_classes = 10)
<- array(runif(20 * 100 * 100 * 3), dim = c(20, 100, 100, 3))
x_test
<- runif(20, min = 0, max = 9) %>%
y_test round() %>%
matrix(nrow = 20, ncol = 1) %>%
to_categorical(num_classes = 10)
# create model
<- keras_model_sequential()
model
# define and compile model
# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
# this applies 32 convolution filters of size 3x3 each.
%>%
model layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu',
input_shape = c(100,100,3)) %>%
layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_dropout(rate = 0.25) %>%
layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>%
layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_dropout(rate = 0.25) %>%
layer_flatten() %>%
layer_dense(units = 256, activation = 'relu') %>%
layer_dropout(rate = 0.25) %>%
layer_dense(units = 10, activation = 'softmax') %>%
compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_sgd(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE)
)
# train
%>% fit(x_train, y_train, batch_size = 32, epochs = 10)
model
# evaluate
<- model %>% evaluate(x_test, y_test, batch_size = 32) score
```

```
<- keras_model_sequential()
model %>%
model layer_embedding(input_dim = max_features, output_dim - 256) %>%
layer_lstm(units = 128) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 1, activation = 'sigmoid') %>%
compile(
loss = 'binary_crossentropy',
optimizer = 'rmsprop',
metrics = c('accuracy')
)
%>% fit(x_train, y_train, batch_size = 16, epochs = 10)
model <- model %>% evaluate(x_test, y_test, batch_size = 16) score
```

```
<- keras_model_sequential()
model %>%
model layer_conv_1d(filters = 64, kernel_size = 3, activation = 'relu',
input_shape = c(seq_length, 100)) %>%
layer_conv_1d(filters = 64, kernel_size = 3, activation = 'relu') %>%
layer_max_pooling_1d(pool_size = 3) %>%
layer_conv_1d(filters = 128, kernel_size = 3, activation = 'relu') %>%
layer_conv_1d(filters = 128, kernel_size = 3, activation = 'relu') %>%
layer_global_average_pooling_1d() %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 1, activation = 'sigmoid') %>%
compile(
loss = 'binary_crossentropy',
optimizer = 'rmsprop',
metrics = c('accuracy')
)
%>% fit(x_train, y_train, batch_size = 16, epochs = 10)
model <- model %>% evaluate(x_test, y_test, batch_size = 16) score
```

In this model, we stack 3 LSTM layers on top of each other, making the model capable of learning higher-level temporal representations.

The first two LSTMs return their full output sequences, but the last one only returns the last step in its output sequence, thus dropping the temporal dimension (i.e. converting the input sequence into a single vector).

```
library(keras)
# constants
<- 16
data_dim <- 8
timesteps <- 10
num_classes
# define and compile model
# expected input data shape: (batch_size, timesteps, data_dim)
<- keras_model_sequential()
model %>%
model layer_lstm(units = 32, return_sequences = TRUE, input_shape = c(timesteps, data_dim)) %>%
layer_lstm(units = 32, return_sequences = TRUE) %>%
layer_lstm(units = 32) %>% # return a single vector dimension 32
layer_dense(units = 10, activation = 'softmax') %>%
compile(
loss = 'categorical_crossentropy',
optimizer = 'rmsprop',
metrics = c('accuracy')
)
# generate dummy training data
<- array(runif(1000 * timesteps * data_dim), dim = c(1000, timesteps, data_dim))
x_train <- matrix(runif(1000 * num_classes), nrow = 1000, ncol = num_classes)
y_train
# generate dummy validation data
<- array(runif(100 * timesteps * data_dim), dim = c(100, timesteps, data_dim))
x_val <- matrix(runif(100 * num_classes), nrow = 100, ncol = num_classes)
y_val
# train
%>% fit(
model batch_size = 64, epochs = 5, validation_data = list(x_val, y_val)
x_train, y_train, )
```

A stateful recurrent model is one for which the internal states (memories) obtained after processing a batch of samples are reused as initial states for the samples of the next batch. This allows to process longer sequences while keeping computational complexity manageable.

You can read more about stateful RNNs in the FAQ.

```
library(keras)
# constants
<- 16
data_dim <- 8
timesteps <- 10
num_classes <- 32
batch_size
# define and compile model
# Expected input batch shape: (batch_size, timesteps, data_dim)
# Note that we have to provide the full batch_input_shape since the network is stateful.
# the sample of index i in batch k is the follow-up for the sample i in batch k-1.
<- keras_model_sequential()
model %>%
model layer_lstm(units = 32, return_sequences = TRUE, stateful = TRUE,
batch_input_shape = c(batch_size, timesteps, data_dim)) %>%
layer_lstm(units = 32, return_sequences = TRUE, stateful = TRUE) %>%
layer_lstm(units = 32, stateful = TRUE) %>%
layer_dense(units = 10, activation = 'softmax') %>%
compile(
loss = 'categorical_crossentropy',
optimizer = 'rmsprop',
metrics = c('accuracy')
)
# generate dummy training data
<- array(runif( (batch_size * 10) * timesteps * data_dim),
x_train dim = c(batch_size * 10, timesteps, data_dim))
<- matrix(runif( (batch_size * 10) * num_classes),
y_train nrow = batch_size * 10, ncol = num_classes)
# generate dummy validation data
<- array(runif( (batch_size * 3) * timesteps * data_dim),
x_val dim = c(batch_size * 3, timesteps, data_dim))
<- matrix(runif( (batch_size * 3) * num_classes),
y_val nrow = batch_size * 3, ncol = num_classes)
# train
%>% fit(
model
x_train,
y_train, batch_size = batch_size,
epochs = 5,
shuffle = FALSE,
validation_data = list(x_val, y_val)
)
```