diktya.func_api_helpers

trainable(model, trainable)

Sets all layers in model to trainable and restores the state afterwards.

Warning

Be aware, that the keras Model.compile method is lazy. You might want to call Model._make_train_function to force a compilation.

Parameters:

• model – keras model
• trainable (bool) – set layer.traiable to this value

Example:

model = Model(x, y)
with trainable(model, False):
    # layers of model are now not trainable
    # Do something
    z = model(y)
    [...]

# now the layers of `model` are trainable again

get_layer(keras_tensor)

Returns the corresponding layer to a keras tensor.

sequential(layers, ns=None, trainable=True)

The functional flexible counter part to the keras Sequential model.

Parameters:

• layers (list) – Can be a arbitrary nested list of layers. The layers will be called sequentially. Can contain None’s
• ns (optional str) – Namespace prefix of the layers
• trainable (optional bool) – set the layer’s trainable attribute to this value.

Returns:

A function that takes a tensor as input, applies all the layers, and returns the output tensor.

Simple example:

Call a list of layers.

x = Input(shape=(32,))
y = sequential([
    Dense(10),
    LeakyReLU(0.4),
    Dense(10, activation='sigmoid'),
])(x)

m = Model(x, y)

Advanced example:

Use a function to construct reoccuring blocks. The conv functions returns a nested list of layers. This allows one to nicely combine and stack different building blocks function.

def conv(n, depth=2, f=3, activation='relu'):
    layers = [
        [
            Convolution2D(n, f, f, border_mode='same'),
            BatchNormalization(),
            Activation(activation)
        ]  for _ in range(depth)
    ]
    return layers + [MaxPooling2D()]

x = Input(shape=(32,))
y = sequential([
    conv(32),
    conv(64),
    conv(128),
    Flatten(),
    Dense(10, activation='sigmoid'),
])(x, ns='classifier')

m = Model(x, y)

concat(tensors, axis=1, **kwargs)

Wrapper around keras merge function.

Parameters:

• tensors – list of keras tensors
• axis – concat on this axis
• kwargs – passed to the merge function

Returns:

The concatenated tensor

rename_layer(keras_tensor, name)

Renames the layer of the keras_tensor

name_tensor(keras_tensor, name)

Add a layer with this name that does nothing.

Usefull to mark a tensor.

keras_copy(obj)

Copies a keras object by using the get_config method.

predict_wrapper(func, names)

save_model(model, fname, overwrite=False, attrs={})

Saves the weights and the config of model in the HDF5 file fname. The model config is saved as: f.attrs["model"] = model.to_json().encode('utf-8'), where f is the HDF5 file.

load_model(fname, custom_objects={})

Loads the model and weights from fname. Counterpart to save_model().

get_hdf5_attr(fname, attr_name, default=None)

Returns the toplevel attribute attr_name of the hdf5 file fname. If default is not None and the attribute is not present, then default is returned.