Commit b8619a06 authored by Mark Hoffmann's avatar Mark Hoffmann

updated annotations

parent 2116793c
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.add.KerasWrap",
......@@ -230,6 +230,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.add.Add",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "ea3131312b2faeede6fd2acf444a30ec2f993dcdfe78a914d614e97ff084228f"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nLayer that adds a list of inputs.\n\n It takes as input a list of tensors,\n all of the same shape, and returns\n a single tensor (also of the same shape).\n\n # Examples\n\n ```python\n import keras\n\n input1 = keras.layers.Input(shape=(16,))\n x1 = keras.layers.Dense(8, activation='relu')(input1)\n input2 = keras.layers.Input(shape=(32,))\n x2 = keras.layers.Dense(8, activation='relu')(input2)\n # equivalent to added = keras.layers.add([x1, x2])\n added = keras.layers.Add()([x1, x2])\n\n out = keras.layers.Dense(4)(added)\n model = keras.models.Model(inputs=[input1, input2], outputs=out)\n ```\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "9c6a22c2686a2f61b416de9d26b59eb374e4ed6ec43e2722c87c99fce2fec95a"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_1d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.average_pooling_1d.AveragePooling1D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Average pooling for temporal data.\n\n # Arguments\n pool_size: Integer, size of the average pooling windows.\n strides: Integer, or None. Factor by which to downscale.\n E.g. 2 will halve the input.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, downsampled_steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, downsampled_steps)`",
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}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAverage pooling for temporal data.\n\n # Arguments\n pool_size: Integer, size of the average pooling windows.\n strides: Integer, or None. Factor by which to downscale.\n E.g. 2 will halve the input.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, downsampled_steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, downsampled_steps)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "b3ad7574953b74b75d36ef60e8f7bcb52951d3ac09fb789542285d66204527ee"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_2d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.average_pooling_2d.AveragePooling2D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Average pooling operation for spatial data.\n\n # Arguments\n pool_size: integer or tuple of 2 integers,\n factors by which to downscale (vertical, horizontal).\n (2, 2) will halve the input in both spatial dimension.\n If only one integer is specified, the same window length\n will be used for both dimensions.\n strides: Integer, tuple of 2 integers, or None.\n Strides values.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, pooled_rows, pooled_cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, pooled_rows, pooled_cols)`",
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}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAverage pooling operation for spatial data.\n\n # Arguments\n pool_size: integer or tuple of 2 integers,\n factors by which to downscale (vertical, horizontal).\n (2, 2) will halve the input in both spatial dimension.\n If only one integer is specified, the same window length\n will be used for both dimensions.\n strides: Integer, tuple of 2 integers, or None.\n Strides values.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, pooled_rows, pooled_cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, pooled_rows, pooled_cols)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "4cf2b426f1ac713efed2b6d425fcc0b82f3f13b93fd013485aa655ef0d19955c"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_3d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.average_pooling_3d.AveragePooling3D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Average pooling operation for 3D data (spatial or spatio-temporal).\n\n # Arguments\n pool_size: tuple of 3 integers,\n factors by which to downscale (dim1, dim2, dim3).\n (2, 2, 2) will halve the size of the 3D input in each dimension.\n strides: tuple of 3 integers, or None. Strides values.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`",
"digest": "0807d2577e7d247efc459092d414ed045ddce0205bf5e90b13b8f29cda47561a"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAverage pooling operation for 3D data (spatial or spatio-temporal).\n\n # Arguments\n pool_size: tuple of 3 integers,\n factors by which to downscale (dim1, dim2, dim3).\n (2, 2, 2) will halve the size of the 3D input in each dimension.\n strides: tuple of 3 integers, or None. Strides values.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "b03ce52860dabd086d4c3d359235af83a1a80373d2f0893e7b2f1c28fe4b8675"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.batch_normalization.KerasWrap",
......@@ -2119,6 +2119,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.batch_normalization.BatchNormalization",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Batch normalization layer (Ioffe and Szegedy, 2014).\n\n Normalize the activations of the previous layer at each batch,\n i.e. applies a transformation that maintains the mean activation\n close to 0 and the activation standard deviation close to 1.\n\n # Arguments\n axis: Integer, the axis that should be normalized\n (typically the features axis).\n For instance, after a `Conv2D` layer with\n `data_format=\"channels_first\"`,\n set `axis=1` in `BatchNormalization`.\n momentum: Momentum for the moving mean and the moving variance.\n epsilon: Small float added to variance to avoid dividing by zero.\n center: If True, add offset of `beta` to normalized tensor.\n If False, `beta` is ignored.\n scale: If True, multiply by `gamma`.\n If False, `gamma` is not used.\n When the next layer is linear (also e.g. `nn.relu`),\n this can be disabled since the scaling\n will be done by the next layer.\n beta_initializer: Initializer for the beta weight.\n gamma_initializer: Initializer for the gamma weight.\n moving_mean_initializer: Initializer for the moving mean.\n moving_variance_initializer: Initializer for the moving variance.\n beta_regularizer: Optional regularizer for the beta weight.\n gamma_regularizer: Optional regularizer for the gamma weight.\n beta_constraint: Optional constraint for the beta weight.\n gamma_constraint: Optional constraint for the gamma weight.\n\n # Input shape\n Arbitrary. Use the keyword argument `input_shape`\n (tuple of integers, does not include the samples axis)\n when using this layer as the first layer in a model.\n\n # Output shape\n Same shape as input.\n\n # References\n - [Batch Normalization: Accelerating Deep Network Training by\n Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)",
"digest": "1d87704a7bdd25805afc0621648aee6bccb170bb69af9356a821ad512c51a94d"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nBatch normalization layer (Ioffe and Szegedy, 2014).\n\n Normalize the activations of the previous layer at each batch,\n i.e. applies a transformation that maintains the mean activation\n close to 0 and the activation standard deviation close to 1.\n\n # Arguments\n axis: Integer, the axis that should be normalized\n (typically the features axis).\n For instance, after a `Conv2D` layer with\n `data_format=\"channels_first\"`,\n set `axis=1` in `BatchNormalization`.\n momentum: Momentum for the moving mean and the moving variance.\n epsilon: Small float added to variance to avoid dividing by zero.\n center: If True, add offset of `beta` to normalized tensor.\n If False, `beta` is ignored.\n scale: If True, multiply by `gamma`.\n If False, `gamma` is not used.\n When the next layer is linear (also e.g. `nn.relu`),\n this can be disabled since the scaling\n will be done by the next layer.\n beta_initializer: Initializer for the beta weight.\n gamma_initializer: Initializer for the gamma weight.\n moving_mean_initializer: Initializer for the moving mean.\n moving_variance_initializer: Initializer for the moving variance.\n beta_regularizer: Optional regularizer for the beta weight.\n gamma_regularizer: Optional regularizer for the gamma weight.\n beta_constraint: Optional constraint for the beta weight.\n gamma_constraint: Optional constraint for the gamma weight.\n\n # Input shape\n Arbitrary. Use the keyword argument `input_shape`\n (tuple of integers, does not include the samples axis)\n when using this layer as the first layer in a model.\n\n # Output shape\n Same shape as input.\n\n # References\n - [Batch Normalization: Accelerating Deep Network Training by\n Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "86ee77c8b7a1eaf18a43cf244929bd33d8a926d28a44d253bf453f18965141e3"
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......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.concat.KerasWrap",
......@@ -230,6 +230,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.concat.Concatenate",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Layer that concatenates a list of inputs.\n\n It takes as input a list of tensors,\n all of the same shape except for the concatenation axis,\n and returns a single tensor, the concatenation of all inputs.\n\n # Arguments\n axis: Axis along which to concatenate.\n **kwargs: standard layer keyword arguments.",
"digest": "8680695665365195a510cdbe5f2df1767df3fa7e9f9b5df33162a785dec70293"
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\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nLayer that concatenates a list of inputs.\n\n It takes as input a list of tensors,\n all of the same shape except for the concatenation axis,\n and returns a single tensor, the concatenation of all inputs.\n\n # Arguments\n axis: Axis along which to concatenate.\n **kwargs: standard layer keyword arguments.\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "465a81e0d2ba881d0bd2c171798eb90b06d30dc7861f05882f22e3e5be8127af"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.dense.KerasWrap",
......@@ -1484,6 +1484,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.dense.Dense",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Just your regular densely-connected NN layer.\n\n `Dense` implements the operation:\n `output = activation(dot(input, kernel) + bias)`\n where `activation` is the element-wise activation function\n passed as the `activation` argument, `kernel` is a weights matrix\n created by the layer, and `bias` is a bias vector created by the layer\n (only applicable if `use_bias` is `True`).\n\n Note: if the input to the layer has a rank greater than 2, then\n it is flattened prior to the initial dot product with `kernel`.\n\n # Example\n\n ```python\n # as first layer in a sequential model:\n model = Sequential()\n model.add(Dense(32, input_shape=(16,)))\n # now the model will take as input arrays of shape (*, 16)\n # and output arrays of shape (*, 32)\n\n # after the first layer, you don't need to specify\n # the size of the input anymore:\n model.add(Dense(32))\n ```\n\n # Arguments\n units: Positive integer, dimensionality of the output space.\n activation: Activation function to use\n (see [activations](../activations.md)).\n If you don't specify anything, no activation is applied\n (ie. \"linear\" activation: `a(x) = x`).\n use_bias: Boolean, whether the layer uses a bias vector.\n kernel_initializer: Initializer for the `kernel` weights matrix\n (see [initializers](../initializers.md)).\n bias_initializer: Initializer for the bias vector\n (see [initializers](../initializers.md)).\n kernel_regularizer: Regularizer function applied to\n the `kernel` weights matrix\n (see [regularizer](../regularizers.md)).\n bias_regularizer: Regularizer function applied to the bias vector\n (see [regularizer](../regularizers.md)).\n activity_regularizer: Regularizer function applied to\n the output of the layer (its \"activation\").\n (see [regularizer](../regularizers.md)).\n kernel_constraint: Constraint function applied to\n the `kernel` weights matrix\n (see [constraints](../constraints.md)).\n bias_constraint: Constraint function applied to the bias vector\n (see [constraints](../constraints.md)).\n\n # Input shape\n nD tensor with shape: `(batch_size, ..., input_dim)`.\n The most common situation would be\n a 2D input with shape `(batch_size, input_dim)`.\n\n # Output shape\n nD tensor with shape: `(batch_size, ..., units)`.\n For instance, for a 2D input with shape `(batch_size, input_dim)`,\n the output would have shape `(batch_size, units)`.",
"digest": "b1d1d8c94bc37472d8ac4ec8208c7874887407fe4893d204555020820d86f738"
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\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nJust your regular densely-connected NN layer.\n\n `Dense` implements the operation:\n `output = activation(dot(input, kernel) + bias)`\n where `activation` is the element-wise activation function\n passed as the `activation` argument, `kernel` is a weights matrix\n created by the layer, and `bias` is a bias vector created by the layer\n (only applicable if `use_bias` is `True`).\n\n Note: if the input to the layer has a rank greater than 2, then\n it is flattened prior to the initial dot product with `kernel`.\n\n # Example\n\n ```python\n # as first layer in a sequential model:\n model = Sequential()\n model.add(Dense(32, input_shape=(16,)))\n # now the model will take as input arrays of shape (*, 16)\n # and output arrays of shape (*, 32)\n\n # after the first layer, you don't need to specify\n # the size of the input anymore:\n model.add(Dense(32))\n ```\n\n # Arguments\n units: Positive integer, dimensionality of the output space.\n activation: Activation function to use\n (see [activations](../activations.md)).\n If you don't specify anything, no activation is applied\n (ie. \"linear\" activation: `a(x) = x`).\n use_bias: Boolean, whether the layer uses a bias vector.\n kernel_initializer: Initializer for the `kernel` weights matrix\n (see [initializers](../initializers.md)).\n bias_initializer: Initializer for the bias vector\n (see [initializers](../initializers.md)).\n kernel_regularizer: Regularizer function applied to\n the `kernel` weights matrix\n (see [regularizer](../regularizers.md)).\n bias_regularizer: Regularizer function applied to the bias vector\n (see [regularizer](../regularizers.md)).\n activity_regularizer: Regularizer function applied to\n the output of the layer (its \"activation\").\n (see [regularizer](../regularizers.md)).\n kernel_constraint: Constraint function applied to\n the `kernel` weights matrix\n (see [constraints](../constraints.md)).\n bias_constraint: Constraint function applied to the bias vector\n (see [constraints](../constraints.md)).\n\n # Input shape\n nD tensor with shape: `(batch_size, ..., input_dim)`.\n The most common situation would be\n a 2D input with shape `(batch_size, input_dim)`.\n\n # Output shape\n nD tensor with shape: `(batch_size, ..., units)`.\n For instance, for a 2D input with shape `(batch_size, input_dim)`,\n the output would have shape `(batch_size, units)`.\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "37c32f66af5e4f964f5bca74327fdf9b32211b52f999a2b9239367fbbf2a96da"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.dropout.KerasWrap",
......@@ -247,6 +247,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.dropout.Dropout",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Applies Dropout to the input.\n\n Dropout consists in randomly setting\n a fraction `rate` of input units to 0 at each update during training time,\n which helps prevent overfitting.\n\n # Arguments\n rate: float between 0 and 1. Fraction of the input units to drop.\n noise_shape: 1D integer tensor representing the shape of the\n binary dropout mask that will be multiplied with the input.\n For instance, if your inputs have shape\n `(batch_size, timesteps, features)` and\n you want the dropout mask to be the same for all timesteps,\n you can use `noise_shape=(batch_size, 1, features)`.\n seed: A Python integer to use as random seed.\n\n # References\n - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting]\n (http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)",
"digest": "74ccbea321109e5d389277ae0260165651b706cfa85f68e3ee10f07faea367c7"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nApplies Dropout to the input.\n\n Dropout consists in randomly setting\n a fraction `rate` of input units to 0 at each update during training time,\n which helps prevent overfitting.\n\n # Arguments\n rate: float between 0 and 1. Fraction of the input units to drop.\n noise_shape: 1D integer tensor representing the shape of the\n binary dropout mask that will be multiplied with the input.\n For instance, if your inputs have shape\n `(batch_size, timesteps, features)` and\n you want the dropout mask to be the same for all timesteps,\n you can use `noise_shape=(batch_size, 1, features)`.\n seed: A Python integer to use as random seed.\n\n # References\n - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting]\n (http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "0d262acda41a121cd4658f99d1f0664a32ea0a6d7a1158c1c894d8d819cce008"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.flatten.KerasWrap",
......@@ -234,6 +234,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.flatten.Flatten",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Flattens the input. Does not affect the batch size.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n The purpose of this argument is to preserve weight\n ordering when switching a model from one data format\n to another.\n `channels_last` corresponds to inputs with shape\n `(batch, ..., channels)` while `channels_first` corresponds to\n inputs with shape `(batch, channels, ...)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Example\n\n ```python\n model = Sequential()\n model.add(Conv2D(64, (3, 3),\n input_shape=(3, 32, 32), padding='same',))\n # now: model.output_shape == (None, 64, 32, 32)\n\n model.add(Flatten())\n # now: model.output_shape == (None, 65536)\n ```",
"digest": "92ad869f0fc364e6d24449f9d77bde6c14ad1454a9dde46e8911d0c1bdb3ad73"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nFlattens the input. Does not affect the batch size.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n The purpose of this argument is to preserve weight\n ordering when switching a model from one data format\n to another.\n `channels_last` corresponds to inputs with shape\n `(batch, ..., channels)` while `channels_first` corresponds to\n inputs with shape `(batch, channels, ...)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Example\n\n ```python\n model = Sequential()\n model.add(Conv2D(64, (3, 3),\n input_shape=(3, 32, 32), padding='same',))\n # now: model.output_shape == (None, 64, 32, 32)\n\n model.add(Flatten())\n # now: model.output_shape == (None, 65536)\n ```\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "f63dce8bbab543f5c0c0cfc68ff3df987d45e4b68c70af88e28859d62984e075"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_1d.KerasWrap",
......@@ -248,6 +248,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.global_average_pooling_1d.GlobalAveragePooling1D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Global average pooling operation for temporal data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, features)`",
"digest": "0208ac2b3f185db7ec8ce8947985b37afe897b5246085a79b0472796aa1781da"
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\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nGlobal average pooling operation for temporal data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, features)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
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......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_2d.KerasWrap",
......@@ -248,6 +248,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.global_average_pooling_2d.GlobalAveragePooling2D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Global average pooling operation for spatial data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, channels)`",
"digest": "bc718f9b44f548cbd455191dcd8a07be7d4c1dc76acc2e7132a5677bd611d3bf"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nGlobal average pooling operation for spatial data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, channels)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "3285daa698c3ff1a3a6627d68414deacbe091708df0fc2c9b8a2da1ec338341c"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_3d.KerasWrap",
......@@ -248,6 +248,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.global_average_pooling_3d.GlobalAveragePooling3D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Global Average pooling operation for 3D data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, channels)`",
"digest": "d9d601d411e1b330867ddb9b021041d3925a4b48942461d56a823eca1f78a448"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nGlobal Average pooling operation for 3D data.\n\n # Arguments\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n 2D tensor with shape:\n `(batch_size, channels)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "14deebc8d469b8f8872e1a2d256ae6971c1a10d29ca85a47d7894d7f9bbd62bc"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_1d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.max_pooling_1d.MaxPooling1D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Max pooling operation for temporal data.\n\n # Arguments\n pool_size: Integer, size of the max pooling windows.\n strides: Integer, or None. Factor by which to downscale.\n E.g. 2 will halve the input.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, downsampled_steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, downsampled_steps)`",
"digest": "b827e084698d352a0f74b0f807b7b118481d7579ca52f3067a9037ff53dc385a"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nMax pooling operation for temporal data.\n\n # Arguments\n pool_size: Integer, size of the max pooling windows.\n strides: Integer, or None. Factor by which to downscale.\n E.g. 2 will halve the input.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, steps, features)` while `channels_first`\n corresponds to inputs with shape\n `(batch, features, steps)`.\n\n # Input shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, steps)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 3D tensor with shape:\n `(batch_size, downsampled_steps, features)`\n - If `data_format='channels_first'`:\n 3D tensor with shape:\n `(batch_size, features, downsampled_steps)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "602c361fd85fb5d12f84ff19fac78a2d1600685122b5e2c1906cb3522c1a676b"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_2d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.max_pooling_2d.MaxPooling2D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Max pooling operation for spatial data.\n\n # Arguments\n pool_size: integer or tuple of 2 integers,\n factors by which to downscale (vertical, horizontal).\n (2, 2) will halve the input in both spatial dimension.\n If only one integer is specified, the same window length\n will be used for both dimensions.\n strides: Integer, tuple of 2 integers, or None.\n Strides values.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, pooled_rows, pooled_cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, pooled_rows, pooled_cols)`",
"digest": "151348f72e213fb81414b095375eee4dfa19f64713d2120a750606bc6db736eb"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nMax pooling operation for spatial data.\n\n # Arguments\n pool_size: integer or tuple of 2 integers,\n factors by which to downscale (vertical, horizontal).\n (2, 2) will halve the input in both spatial dimension.\n If only one integer is specified, the same window length\n will be used for both dimensions.\n strides: Integer, tuple of 2 integers, or None.\n Strides values.\n If None, it will default to `pool_size`.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, height, width, channels)` while `channels_first`\n corresponds to inputs with shape\n `(batch, channels, height, width)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, rows, cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, rows, cols)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 4D tensor with shape:\n `(batch_size, pooled_rows, pooled_cols, channels)`\n - If `data_format='channels_first'`:\n 4D tensor with shape:\n `(batch_size, channels, pooled_rows, pooled_cols)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "f6a754bdb445b25d25445a0fa8ce588cb0350796b441035a534ff0ff58cd4df5"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_3d.KerasWrap",
......@@ -291,6 +291,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.max_pooling_3d.MaxPooling3D",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Max pooling operation for 3D data (spatial or spatio-temporal).\n\n # Arguments\n pool_size: tuple of 3 integers,\n factors by which to downscale (dim1, dim2, dim3).\n (2, 2, 2) will halve the size of the 3D input in each dimension.\n strides: tuple of 3 integers, or None. Strides values.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`",
"digest": "6b2f87ff9f605f2b15af1959a285ee5d1a9b6677bf25fc1a9946634a960f30a3"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nMax pooling operation for 3D data (spatial or spatio-temporal).\n\n # Arguments\n pool_size: tuple of 3 integers,\n factors by which to downscale (dim1, dim2, dim3).\n (2, 2, 2) will halve the size of the 3D input in each dimension.\n strides: tuple of 3 integers, or None. Strides values.\n padding: One of `\"valid\"` or `\"same\"` (case-insensitive).\n data_format: A string,\n one of `channels_last` (default) or `channels_first`.\n The ordering of the dimensions in the inputs.\n `channels_last` corresponds to inputs with shape\n `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n while `channels_first` corresponds to inputs with shape\n `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.\n It defaults to the `image_data_format` value found in your\n Keras config file at `~/.keras/keras.json`.\n If you never set it, then it will be \"channels_last\".\n\n # Input shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`\n\n # Output shape\n - If `data_format='channels_last'`:\n 5D tensor with shape:\n `(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`\n - If `data_format='channels_first'`:\n 5D tensor with shape:\n `(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "8756d9de197a0ddb8f9120700a2bda67c67e8c1e2ba13d56806eeb66a5f44b11"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.null.KerasWrap",
......@@ -205,5 +205,5 @@
},
"structural_type": "jpl_primitives.keras_wrap.layers.null.Null",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.This is a special Null primitive to avoid circular imports with defaults.",
"digest": "f2eec33e688034dc861d0a58948f6c39a5a5ec97d04ff79ac674e7e8350f6a6d"
}
\ No newline at end of file
"digest": "3807fa70936e1344b1975d970ba4a88d29e8a6846842fdc4b2d8eb9bf152614a"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@16e40a6f49ed816f6cc520380ab2912f0d900486#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@80e7de3b9c4a0aff334484ed2e9e128b3ea1a3f6#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.subtract.KerasWrap",
......@@ -260,6 +260,6 @@
"params": {}
},
"structural_type": "jpl_primitives.keras_wrap.layers.subtract.Subtract",
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.Layer that subtracts two inputs.\n\n It takes as input a list of tensors of size 2,\n both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]),\n also of the same shape.\n\n # Examples\n\n ```python\n import keras\n\n input1 = keras.layers.Input(shape=(16,))\n x1 = keras.layers.Dense(8, activation='relu')(input1)\n input2 = keras.layers.Input(shape=(32,))\n x2 = keras.layers.Dense(8, activation='relu')(input2)\n # Equivalent to subtracted = keras.layers.subtract([x1, x2])\n subtracted = keras.layers.Subtract()([x1, x2])\n\n out = keras.layers.Dense(4)(subtracted)\n model = keras.models.Model(inputs=[input1, input2], outputs=out)\n ```",
"digest": "6ab62ff58817f420855e481d76779f1e4e2720725067634397fecdbbe9a9aa26"
}
\ No newline at end of file
"description": "A neural network layer that has been wrapped from Keras. You can assemble these layers togetherto form any architecture\nnerual network. To assemble, every layer has a hyperparameter 'previous_layer'. This hyperparameter takes in another Keras wrapped\nlayer primitive and you are allowed to chain your neural network together. This is chained until the very first layer you\nwant to serve as your initial input layer.\n\nPure Keras Documentation:\n\nLayer that subtracts two inputs.\n\n It takes as input a list of tensors of size 2,\n both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]),\n also of the same shape.\n\n # Examples\n\n ```python\n import keras\n\n input1 = keras.layers.Input(shape=(16,))\n x1 = keras.layers.Dense(8, activation='relu')(input1)\n input2 = keras.layers.Input(shape=(32,))\n x2 = keras.layers.Dense(8, activation='relu')(input2)\n # Equivalent to subtracted = keras.layers.subtract([x1, x2])\n subtracted = keras.layers.Subtract()([x1, x2])\n\n out = keras.layers.Dense(4)(subtracted)\n model = keras.models.Model(inputs=[input1, input2], outputs=out)\n ```\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "0e59e84aa9bbe7b258e01fea04bc26b703112c3c973b28978f913042040d61ec"
}
context: TESTING
created: '2019-04-26T01:09:44.343543Z'
created: "2019-04-26T01:09:44.343543Z"
id: 32418dc0-a64d-4cc6-af3f-671a828a22e3
inputs:
-
name: inputs
- name: inputs
outputs:
-
data: steps.10.produce
name: 'output predictions'
schema: 'https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json'
- data: steps.9.produce
name: "output predictions"
schema: "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json"
steps:
-
arguments:
- arguments:
inputs:
data: inputs.0
type: CONTAINER
outputs:
-
id: produce
- id: produce
primitive:
id: f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e
name: 'Denormalize datasets'
name: "Denormalize datasets"
python_path: d3m.primitives.data_transformation.denormalize.Common
version: 0.2.0
type: PRIMITIVE
-
arguments:
- arguments:
inputs:
data: steps.0.produce
type: CONTAINER
outputs:
-
id: produce
- id: produce
primitive:
id: 4b42ce1e-9b98-4a25-b68e-fad13311eb65
name: 'Extract a DataFrame from a Dataset'
name: "Extract a DataFrame from a Dataset"
python_path: d3m.primitives.data_transformation.dataset_to_dataframe.Common
version: 0.3.0
type: PRIMITIVE
-
arguments:
- arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
- id: produce
primitive:
id: 8f2e51e8-da59-456d-ae29-53912b2b9f3d
name: 'Columns image reader'
name: "Columns image reader"
python_path: d3m.primitives.data_preprocessing.image_reader.DataFrameCommon
version: 0.2.0
type: PRIMITIVE
-
type: PRIMITIVE
- type: PRIMITIVE
primitive:
id: e8acc97a-7868-427e-b022-e2aa51116d19
version: 0.1.0
python_path: d3m.primitives.layer.flatten.KerasWrap
name: flatten
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
-
type: PRIMITIVE
- type: PRIMITIVE
primitive:
id: 393ac0d9-8d4d-4b2a-85dc-35815e8a6695
version: 0.1.0
python_path: d3m.primitives.loss_function.categorical_accuracy.KerasWrap
name: categorical_accuracy
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
-
type: PRIMITIVE
- type: PRIMITIVE
primitive:
id: 01cee53a-88e3-4bf3-993a-fd64805e9b8e
version: 0.1.0
python_path: d3m.primitives.loss_function.categorical_crossentropy.KerasWrap
name: categorical_crossentropy
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
-
type: PRIMITIVE
- type: PRIMITIVE
hyperparams:
units:
data: 100
......@@ -117,18 +75,7 @@ steps:
version: 0.1.0
python_path: d3m.primitives.layer.dense.KerasWrap
name: dense
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
-
type: PRIMITIVE
- type: PRIMITIVE
hyperparams:
units:
data: 10
......@@ -141,42 +88,12 @@ steps:
version: 0.1.0
python_path: d3m.primitives.layer.dense.KerasWrap
name: dense
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.1.produce
type: CONTAINER
outputs:
-
id: produce
-
arguments:
inputs:
data: steps.1.produce
type: CONTAINER
hyperparams:
semantic_types:
data:
- 'https://metadata.datadrivendiscovery.org/types/TrueTarget'
type: VALUE
outputs:
-
id: produce
primitive:
id: 4503a4c6-42f7-45a1-a1d4-ed69699cf5e1
name: 'Extracts columns by semantic type'
python_path: d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon
version: 0.2.0
type: PRIMITIVE
-
arguments:
- arguments:
inputs:
data: steps.2.produce
type: CONTAINER
outputs:
data: steps.8.produce
data: steps.2.produce
type: CONTAINER
hyperparams:
return_result:
......@@ -194,38 +111,26 @@ steps:
previous_layer:
data: 7
type: PRIMITIVE
input_width:
data: 28
type: VALUE
input_height:
data: 28
type: VALUE
input_channels:
data: 1
type: VALUE
outputs:
-