---
title: The Sequential model
date-created: 2020/04/12
last-modified: 2023/06/25
description: Complete guide to the Sequential model.
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{The Sequential model}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Setup


``` r
library(keras3)
```

## When to use a Sequential model

A `Sequential` model is appropriate for **a plain stack of layers**
where each layer has **exactly one input tensor and one output tensor**.

Schematically, the following `Sequential` model:


``` r
model <- keras_model_sequential() |>
  layer_dense(units = 2, activation = "relu", name = "layer1") |>
  layer_dense(units = 3, activation = "relu", name = "layer2") |>
  layer_dense(units = 4, name = "layer3")

# Call model on a test input
x <- op_ones(c(3, 3))
y <- model(x)
```

is equivalent to this function:


``` r
# Create 3 layers
layer1 <- layer_dense(units = 2, activation="relu", name="layer1")
layer2 <- layer_dense(units = 3, activation="relu", name="layer2")
layer3 <- layer_dense(units = 4, name="layer3")

# Call layers on a test input
x <- op_ones(c(3, 3))
y <- x |> layer1() |> layer2() |> layer3()
```

A Sequential model is **not appropriate** when:

- Your model has multiple inputs or multiple outputs
- Any of your layers has multiple inputs or multiple outputs
- You need to do layer sharing
- You want non-linear topology (e.g. a residual connection, a multi-branch
model)

## Creating a Sequential model

You can create a Sequential model by piping layers into the `keras_model_sequential()`
object:


``` r
model <- keras_model_sequential() |>
  layer_dense(units = 2, activation = "relu") |>
  layer_dense(units = 3, activation = "relu") |>
  layer_dense(units = 4)
```

or by passing a list of layers to `keras_model_sequential()`:


``` r
model <- keras_model_sequential(layers = list(
  layer_dense(units = 2, activation = "relu"),
  layer_dense(units = 3, activation = "relu"),
  layer_dense(units = 4)
))
```

Its layers are accessible via the `layers` attribute:


``` r
model$layers
```

```
## [[1]]
## <Dense name=dense_3, built=False>
##  signature: (*args, **kwargs)
##
## [[2]]
## <Dense name=dense_4, built=False>
##  signature: (*args, **kwargs)
##
## [[3]]
## <Dense name=dense_5, built=False>
##  signature: (*args, **kwargs)
```

You can also create a Sequential model incrementally:


``` r
model <- keras_model_sequential()
model |> layer_dense(units = 2, activation="relu")
model |> layer_dense(units = 3, activation="relu")
model |> layer_dense(units = 4)
```

Note that there's also a corresponding `pop_layer()` method to remove layers:
a Sequential model behaves very much like a stack of layers.


``` r
model |> pop_layer()
length(model$layers)  # 2
```

```
## [1] 2
```

Also note that the Sequential constructor accepts a `name` argument, just like
any layer or model in Keras. This is useful to annotate TensorBoard graphs
with semantically meaningful names.


``` r
model <- keras_model_sequential(name = "my_sequential")
model |> layer_dense(units = 2, activation="relu", name = "layer1")
model |> layer_dense(units = 3, activation="relu", name = "layer2")
model |> layer_dense(units = 4, name = "layer3")
```

## Specifying the input shape in advance

Generally, all layers in Keras need to know the shape of their inputs
in order to be able to create their weights. So when you create a layer like
this, initially, it has no weights:


``` r
layer <- layer_dense(units = 3)
layer$weights  # Empty
```

```
## list()
```

It creates its weights the first time it is called on an input, since the shape
of the weights depends on the shape of the inputs:


``` r
# Call layer on a test input
x <- op_ones(c(1, 4))
y <- layer(x)
layer$weights  # Now it has weights, of shape (4, 3) and (3,)
```

```
## [[1]]
## <Variable path=dense_9/kernel, shape=(4, 3), dtype=float32, value=[[ 0.48581433  0.78749573  0.61015   ]
##  [ 0.7962619   0.7261175  -0.8046875 ]
##  [-0.6189915   0.37973273  0.50559556]
##  [-0.5455791  -0.60714126  0.19791973]]>
##
## [[2]]
## <Variable path=dense_9/bias, shape=(3), dtype=float32, value=[0. 0. 0.]>
```

Naturally, this also applies to Sequential models. When you instantiate a
Sequential model without an input shape, it isn't "built": it has no weights
(and calling
`model$weights` results in an error stating just this). The weights are created
when the model first sees some input data:


``` r
model <- keras_model_sequential() |>
  layer_dense(units = 2, activation = "relu") |>
  layer_dense(units = 3, activation = "relu") |>
  layer_dense(units = 4)
# No weights at this stage!

# At this point, you can't do this:
# model$weights


# Call the model on a test input
x <- op_ones(c(1, 4))
y <- model(x)
length(model$weights)
```

```
## [1] 6
```

Once a model is "built", you can call its `summary()` method to display its
contents:


``` r
summary(model)
```

```
## [1mModel: "sequential_4"[0m
## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
## ┃[1m [0m[1mLayer (type)                   [0m[1m [0m┃[1m [0m[1mOutput Shape          [0m[1m [0m┃[1m [0m[1m      Param #[0m[1m [0m┃
## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
## │ dense_10 ([38;5;33mDense[0m)                │ ([38;5;34m1[0m, [38;5;34m2[0m)                 │            [38;5;34m10[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ dense_11 ([38;5;33mDense[0m)                │ ([38;5;34m1[0m, [38;5;34m3[0m)                 │             [38;5;34m9[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ dense_12 ([38;5;33mDense[0m)                │ ([38;5;34m1[0m, [38;5;34m4[0m)                 │            [38;5;34m16[0m │
## └─────────────────────────────────┴────────────────────────┴───────────────┘
## [1m Total params: [0m[38;5;34m35[0m (140.00 B)
## [1m Trainable params: [0m[38;5;34m35[0m (140.00 B)
## [1m Non-trainable params: [0m[38;5;34m0[0m (0.00 B)
```

However, it can be very useful when building a Sequential model incrementally
to be able to display the summary of the model so far, including the current
output shape. In this case, you should start your model by passing an `input_shape`
argument to your model, so that it knows its input shape from the start:


``` r
model <- keras_model_sequential(input_shape = 4) |>
  layer_dense(units = 2, activation = "relu")
summary(model)
```

```
## [1mModel: "sequential_5"[0m
## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
## ┃[1m [0m[1mLayer (type)                   [0m[1m [0m┃[1m [0m[1mOutput Shape          [0m[1m [0m┃[1m [0m[1m      Param #[0m[1m [0m┃
## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
## │ dense_13 ([38;5;33mDense[0m)                │ ([38;5;45mNone[0m, [38;5;34m2[0m)              │            [38;5;34m10[0m │
## └─────────────────────────────────┴────────────────────────┴───────────────┘
## [1m Total params: [0m[38;5;34m10[0m (40.00 B)
## [1m Trainable params: [0m[38;5;34m10[0m (40.00 B)
## [1m Non-trainable params: [0m[38;5;34m0[0m (0.00 B)
```


``` r
model$layers
```

```
## [[1]]
## <Dense name=dense_13, built=True>
##  signature: (*args, **kwargs)
```

Models built with a predefined input shape like this always have weights (even
before seeing any data) and always have a defined output shape.

In general, it's a recommended best practice to always specify the input shape
of a Sequential model in advance if you know what it is.

## A common debugging workflow: add layers + `summary()`

When building a new Sequential architecture, it's useful to incrementally stack
layers with `|>` and frequently print model summaries. For instance, this
enables you to monitor how a stack of `Conv2D` and `MaxPooling2D` layers is
downsampling image feature maps:


``` r
model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>
  layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_max_pooling_2d(pool_size = c(3, 3))

# Can you guess what the current output shape is at this point? Probably not.
# Let's just print it:
summary(model)
```

```
## [1mModel: "sequential_6"[0m
## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
## ┃[1m [0m[1mLayer (type)                   [0m[1m [0m┃[1m [0m[1mOutput Shape          [0m[1m [0m┃[1m [0m[1m      Param #[0m[1m [0m┃
## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
## │ conv2d ([38;5;33mConv2D[0m)                 │ ([38;5;45mNone[0m, [38;5;34m123[0m, [38;5;34m123[0m, [38;5;34m32[0m)   │         [38;5;34m2,432[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_1 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m121[0m, [38;5;34m121[0m, [38;5;34m32[0m)   │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ max_pooling2d ([38;5;33mMaxPooling2D[0m)    │ ([38;5;45mNone[0m, [38;5;34m40[0m, [38;5;34m40[0m, [38;5;34m32[0m)     │             [38;5;34m0[0m │
## └─────────────────────────────────┴────────────────────────┴───────────────┘
## [1m Total params: [0m[38;5;34m11,680[0m (45.62 KB)
## [1m Trainable params: [0m[38;5;34m11,680[0m (45.62 KB)
## [1m Non-trainable params: [0m[38;5;34m0[0m (0.00 B)
```

``` r
# The answer was: (40, 40, 32), so we can keep downsampling...

model |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_max_pooling_2d(pool_size = 3) |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_max_pooling_2d(pool_size = 2)

# And now?
summary(model)
```

```
## [1mModel: "sequential_6"[0m
## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
## ┃[1m [0m[1mLayer (type)                   [0m[1m [0m┃[1m [0m[1mOutput Shape          [0m[1m [0m┃[1m [0m[1m      Param #[0m[1m [0m┃
## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
## │ conv2d ([38;5;33mConv2D[0m)                 │ ([38;5;45mNone[0m, [38;5;34m123[0m, [38;5;34m123[0m, [38;5;34m32[0m)   │         [38;5;34m2,432[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_1 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m121[0m, [38;5;34m121[0m, [38;5;34m32[0m)   │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ max_pooling2d ([38;5;33mMaxPooling2D[0m)    │ ([38;5;45mNone[0m, [38;5;34m40[0m, [38;5;34m40[0m, [38;5;34m32[0m)     │             [38;5;34m0[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_2 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m38[0m, [38;5;34m38[0m, [38;5;34m32[0m)     │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_3 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m36[0m, [38;5;34m36[0m, [38;5;34m32[0m)     │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ max_pooling2d_1 ([38;5;33mMaxPooling2D[0m)  │ ([38;5;45mNone[0m, [38;5;34m12[0m, [38;5;34m12[0m, [38;5;34m32[0m)     │             [38;5;34m0[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_4 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m10[0m, [38;5;34m10[0m, [38;5;34m32[0m)     │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ conv2d_5 ([38;5;33mConv2D[0m)               │ ([38;5;45mNone[0m, [38;5;34m8[0m, [38;5;34m8[0m, [38;5;34m32[0m)       │         [38;5;34m9,248[0m │
## ├─────────────────────────────────┼────────────────────────┼───────────────┤
## │ max_pooling2d_2 ([38;5;33mMaxPooling2D[0m)  │ ([38;5;45mNone[0m, [38;5;34m4[0m, [38;5;34m4[0m, [38;5;34m32[0m)       │             [38;5;34m0[0m │
## └─────────────────────────────────┴────────────────────────┴───────────────┘
## [1m Total params: [0m[38;5;34m48,672[0m (190.12 KB)
## [1m Trainable params: [0m[38;5;34m48,672[0m (190.12 KB)
## [1m Non-trainable params: [0m[38;5;34m0[0m (0.00 B)
```

``` r
# Now that we have 4x4 feature maps, time to apply global max pooling.
model |>
  layer_global_max_pooling_2d()

# Finally, we add a classification layer.
model |>
  layer_dense(units = 10, activation = "softmax")
```

Very practical, right?

Note that `|>` is equivalent to calling `model$add()`, it modifies the model
in-place, so you don't need to reassign the `model` symbol at each step.

## What to do once you have a model

Once your model architecture is ready, you will want to:

- Train your model, evaluate it, and run inference. See our
[guide to training & evaluation with the built-in loops](
    training_with_built_in_methods.html)
- Save your model to disk and restore it. See our
[guide to serialization & saving](serialization_and_saving.html).

## Feature extraction with a Sequential model

Once a Sequential model has been built, it behaves like a [Functional API
model](functional_api.html).
This means that every layer has an `input`
and `output` attribute. These attributes can be used to do neat things, like
quickly creating a model that extracts the outputs of all intermediate layers in a
Sequential model:


``` r
initial_model <- keras_model_sequential(input_shape = c(250, 250, 3)) |>
  layer_conv_2d(filters = 32, kernel_size = 5, strides = 2, activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu")


feature_extractor <- keras_model(
    inputs = initial_model$inputs,
    outputs = lapply(initial_model$layers, function(x) x$output),
)

# Call feature extractor on test input.
x <- op_ones(c(1, 250, 250, 3))
features <- feature_extractor(x)
```

Here's a similar example that only extract features from one layer:


``` r
initial_model <-
  keras_model_sequential(input_shape = c(250, 250, 3)) |>
  layer_conv_2d(filters = 32, kernel_size = 5, strides = 2,
                activation = "relu") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu",
                name = "my_intermediate_layer") |>
  layer_conv_2d(filters = 32, kernel_size = 3, activation = "relu")

feature_extractor <- keras_model(
  inputs = initial_model$inputs,
  outputs = get_layer(initial_model, "my_intermediate_layer")$output,
)

# Call feature extractor on test input.
x <- op_ones(c(1, 250, 250, 3))
features <- feature_extractor(x)
```

## Transfer learning with a Sequential model

Transfer learning consists of freezing the bottom layers in a model and only training
the top layers. If you aren't familiar with it, make sure to read our [guide
to transfer learning](transfer_learning.html).

Here are two common transfer learning blueprint involving Sequential models.

First, let's say that you have a Sequential model, and you want to freeze all
layers except the last one. In this case, you can call `freeze_weights()`. Alternatively, you
can iterate over
`model$layers` and set `layer$trainable <- FALSE` on each layer, except the
last one. Like this:


``` r
model <- keras_model_sequential(input_shape = 784) |>
  layer_dense(units = 32, activation = "relu") |>
  layer_dense(units = 32, activation = "relu") |>
  layer_dense(units = 32, activation = "relu") |>
  layer_dense(units = 10)
```

``` r
# Presumably you would want to first load pre-trained weights.
model |> load_model_weights(...)
```

``` r
# Freeze all layers except the last one.
model |> freeze_weights(from = 1, to = -2)
model # note the "Trainable" column now visible in the summary table
```

```
## [1mModel: "sequential_9"[0m
## ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓
## ┃[1m [0m[1mLayer (type)               [0m[1m [0m┃[1m [0m[1mOutput Shape         [0m[1m [0m┃[1m [0m[1m   Param #[0m[1m [0m┃[1m [0m[1mTrai…[0m[1m [0m┃
## ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩
## │ dense_15 ([38;5;33mDense[0m)            │ ([38;5;45mNone[0m, [38;5;34m32[0m)            │     [38;5;34m25,120[0m │   [1;91mN[0m   │
## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤
## │ dense_16 ([38;5;33mDense[0m)            │ ([38;5;45mNone[0m, [38;5;34m32[0m)            │      [38;5;34m1,056[0m │   [1;91mN[0m   │
## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤
## │ dense_17 ([38;5;33mDense[0m)            │ ([38;5;45mNone[0m, [38;5;34m32[0m)            │      [38;5;34m1,056[0m │   [1;91mN[0m   │
## ├─────────────────────────────┼───────────────────────┼────────────┼───────┤
## │ dense_18 ([38;5;33mDense[0m)            │ ([38;5;45mNone[0m, [38;5;34m10[0m)            │        [38;5;34m330[0m │   [1;38;5;34mY[0m   │
## └─────────────────────────────┴───────────────────────┴────────────┴───────┘
## [1m Total params: [0m[38;5;34m27,562[0m (107.66 KB)
## [1m Trainable params: [0m[38;5;34m330[0m (1.29 KB)
## [1m Non-trainable params: [0m[38;5;34m27,232[0m (106.38 KB)
```

``` r
# Another way to freeze all layers except the last one.
for (layer in model$layers[-length(model$layers)]) {
  layer$trainable <- FALSE
}

# Recompile and train (this will only update the weights of the last layer).
model |> compile(...)
model |> fit(...)
```

Another common blueprint is to use a Sequential model to stack a pre-trained
model and some freshly initialized classification layers. Like this:


``` r
# Load a convolutional base with pre-trained weights
base_model <- application_xception(weights = 'imagenet',
                                   include_top = FALSE,
                                   pooling = 'avg')

# Freeze the base model
freeze_weights(base_model)

# Use a Sequential model to add a trainable classifier on top
model <- keras_model_sequential() |>
  base_model() |>
  layer_dense(1000)
```


``` r
# Compile & train
model |> compile(...)
model |> fit(...)
```


If you do transfer learning, you will probably find yourself frequently using
these two patterns.

That's about all you need to know about Sequential models!

To find out more about building models in Keras, see:

- [Guide to the Functional API](functional_api.html)
- [Guide to making new Layers & Models via subclassing](making_new_layers_and_models_via_subclassing.html)
