Linear stack of layers. Arguments: layers: list of layers to add to the model. Example: ```python # Optionally, the first layer can receive an `input_shape` argument: model = Sequential() model.add(Dense(32, input_shape=(500,))) # Afterwards, we do automatic shape inference:
| 38 | |
| 39 | @keras_export('keras.models.Sequential', 'keras.Sequential') |
| 40 | class Sequential(training.Model): |
| 41 | """Linear stack of layers. |
| 42 | |
| 43 | Arguments: |
| 44 | layers: list of layers to add to the model. |
| 45 | |
| 46 | Example: |
| 47 | |
| 48 | ```python |
| 49 | # Optionally, the first layer can receive an `input_shape` argument: |
| 50 | model = Sequential() |
| 51 | model.add(Dense(32, input_shape=(500,))) |
| 52 | # Afterwards, we do automatic shape inference: |
| 53 | model.add(Dense(32)) |
| 54 | |
| 55 | # This is identical to the following: |
| 56 | model = Sequential() |
| 57 | model.add(Dense(32, input_dim=500)) |
| 58 | |
| 59 | # And to the following: |
| 60 | model = Sequential() |
| 61 | model.add(Dense(32, batch_input_shape=(None, 500))) |
| 62 | |
| 63 | # Note that you can also omit the `input_shape` argument: |
| 64 | # In that case the model gets built the first time you call `fit` (or other |
| 65 | # training and evaluation methods). |
| 66 | model = Sequential() |
| 67 | model.add(Dense(32)) |
| 68 | model.add(Dense(32)) |
| 69 | model.compile(optimizer=optimizer, loss=loss) |
| 70 | # This builds the model for the first time: |
| 71 | model.fit(x, y, batch_size=32, epochs=10) |
| 72 | |
| 73 | # Note that when using this delayed-build pattern (no input shape specified), |
| 74 | # the model doesn't have any weights until the first call |
| 75 | # to a training/evaluation method (since it isn't yet built): |
| 76 | model = Sequential() |
| 77 | model.add(Dense(32)) |
| 78 | model.add(Dense(32)) |
| 79 | model.weights # returns [] |
| 80 | |
| 81 | # Whereas if you specify the input shape, the model gets built continuously |
| 82 | # as you are adding layers: |
| 83 | model = Sequential() |
| 84 | model.add(Dense(32, input_shape=(500,))) |
| 85 | model.add(Dense(32)) |
| 86 | model.weights # returns list of length 4 |
| 87 | |
| 88 | # When using the delayed-build pattern (no input shape specified), you can |
| 89 | # choose to manually build your model by calling `build(batch_input_shape)`: |
| 90 | model = Sequential() |
| 91 | model.add(Dense(32)) |
| 92 | model.add(Dense(32)) |
| 93 | model.build((None, 500)) |
| 94 | model.weights # returns list of length 4 |
| 95 | ``` |
| 96 | """ |
| 97 |
no outgoing calls