Commit 01a17b51 authored by Mark Hoffmann's avatar Mark Hoffmann

updated annotations

parent 90fb5592
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -342,5 +342,5 @@
},
"structural_type": "jpl_primitives.tpot.SKOneHotEncoder.SKOneHotEncoder",
"description": "Primitive wrapping for sklearn OneHotEncoder\n`tpot documentation <https://github.com/EpistasisLab/tpot/blob/master/tpot/builtins/one_hot_encoder.py>`_\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": "7cfb8f7fec6858a1a022caa401a821d6b3fd5dfcb5aee4403aaf3a02e480e09d"
"digest": "358b946df5b48734344524c4d105430ab9c7f16c04edec66ced85625717d3b2b"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -277,5 +277,5 @@
},
"structural_type": "jpl_primitives.tpot.SKCategoricalSelector.SKCategoricalSelector",
"description": "Primitive wrapping for TPOT CategoricalSelector\nGenerated from TPOT commit: https://github.com/EpistasisLab/tpot/commit/1764731234d47849456e6a59c935bc9ec7c62c8e#diff-006b5a271ae3e6b6735939a2c66f0ef1\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": "d5955bc7db011efff78828145def54efc2a440a3ebab8bff758be0182ddc3ce0"
"digest": "8a60438e26a4155bccab86988df4edb31ca5e1cb1fd03ced7ef69b6a39987862"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -277,5 +277,5 @@
},
"structural_type": "jpl_primitives.tpot.SKContinuousSelector.SKContinuousSelector",
"description": "Primitive wrapping for TPOT ContinuousSelector\nGenerated from TPOT commit: https://github.com/EpistasisLab/tpot/commit/1764731234d47849456e6a59c935bc9ec7c62c8e#diff-006b5a271ae3e6b6735939a2c66f0ef1\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": "8a6d2f96a1dc73a7578d48bef4bb140e1c63e28a0295a780896ae8cb17408e17"
"digest": "c407b4b4a7eeed0aa298703b17443de063739880108b93ffa13cf33582a21494"
}
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/primitive.json",
......@@ -264,5 +264,5 @@
},
"structural_type": "jpl_primitives.tpot.SKZeroCount.SKZeroCount",
"description": "Primitive wrapping for TPOT ZeroCount\nGenerated from TPOT commit: https://github.com/EpistasisLab/tpot/commit/1764731234d47849456e6a59c935bc9ec7c62c8e#diff-006b5a271ae3e6b6735939a2c66f0ef1\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": "4e74e6bd707f5abda46f7ded323d79fa8ceb8cc0729d20cc16c55ae2ab42c4b9"
"digest": "6b51caf0b021e0c128bf0ac327e9ef8a5bceb4c60975b6f1ab336c7e9e38c6cd"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/add.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/add.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.add.KerasWrap",
......@@ -231,5 +231,5 @@
},
"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\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": "f5a6355b1b5c16492d12b0822010c3f3770209ec2f3d317a050dc3c8c9411154"
"digest": "4ae573af17c514bb40238b345e0b965302b24b21ee1eee8df34b4b1fc3d91749"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/average_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/average_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_1d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "7b027e33af4def3c611a85c770116d14302568c90a1274bde0135e961d64ad32"
"digest": "99f22a12d98ec46cb8ac9a2cb5b1dba48fb7e10efb2773dad4566e11dfda111a"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/average_pooling_2d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/average_pooling_2d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_2d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "6465194ae2ca0ecd720b52d8f022c4f9d0ed13bcdbc8023ec53f5a7598b87c2c"
"digest": "3a2e5b15d602ee8ef8bbbbaaf05cf31d18bc06ab2cf0926de4e003948d1d8ff1"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/average_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/average_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.average_pooling_3d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "b01d9e98eb7512ad7dbcd3e9043153d90b7210c9e49e3dda85626b2eff5ed37f"
"digest": "1288f001c482b364ee1e34ec7ee8cdf2394e9c741b33cadd62fac6667af159f7"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/concat.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/concat.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.concat.KerasWrap",
......@@ -231,5 +231,5 @@
},
"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\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": "0f3ea932436bdc2b572b505188e78f0448979e7007d0a1ea0274c77955d27c93"
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}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/dropout.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/dropout.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.dropout.KerasWrap",
......@@ -248,5 +248,5 @@
},
"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\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.",
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}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/flatten.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/flatten.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.flatten.KerasWrap",
......@@ -235,5 +235,5 @@
},
"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\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.",
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}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/global_average_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/global_average_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_1d.KerasWrap",
......@@ -249,5 +249,5 @@
},
"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\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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}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/global_average_pooling_2d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/global_average_pooling_2d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_2d.KerasWrap",
......@@ -249,5 +249,5 @@
},
"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\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": "3cae99291816faaaff3da58115fb53a31d152f036806978ad6c48f4604445c40"
"digest": "084118c0742ee4935f1e39be8a13b97185fe4d17fed74a04d0b95225f8297ad5"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/global_average_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/global_average_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.global_average_pooling_3d.KerasWrap",
......@@ -249,5 +249,5 @@
},
"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\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": "5af78665d288add4d08ec9188caf2b5b87e4cd3709a7afc23687c4d6b0c81204"
"digest": "1d79d00ba4f9813643dbdd5f737a24bcf54a5c8a6c085d6ce112fbf4e956ae5b"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/max_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/max_pooling_1d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_1d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "36659606592275db43a00e3f127ae71970b4e36bd2a64cd6d28d9bd08c92c3ca"
"digest": "e3155f27fb2253b0d881da00f97ce2940e529cc74f72553f70909178f50e3d43"
}
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@84ce0b821d4a7e421fa9946fd2a68f78fc86518f#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_2d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "c88af380a53cd26e3694e4a606bccdad8a8993f4bb4a130604b2d6f9c1935ab3"
"digest": "72fd322a8cfe93cdea2f39f315d4b47a314f9ef2bc43893ab3711725374b0aca"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/max_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/max_pooling_3d.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.max_pooling_3d.KerasWrap",
......@@ -292,5 +292,5 @@
},
"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\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": "416769aefd2039ed3379d6da81e16b117dc69b1180d40a6c16b9d00650c766fe"
"digest": "15c272d7a70f86586d0b0472bac6b4682d9d360ad72bf3e07a7696e805aabac3"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/null.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/null.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#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": "bb2af1fe784549b8f4e3be8e8cde78fd5506c31eea3572e01c7ac9a953cb4a7b"
"digest": "55dea354edd60c5931fd72340be4d530d9aaf41494633b118d416703931dab60"
}
......@@ -10,14 +10,14 @@
"name": "JPL-manual",
"contact": "mailto:mark.k.hoffmann@jpl.nasa.gov",
"uris": [
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/subtract.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives/blob/master/jpl_primitives/keras_wrap/layers/subtract.py",
"https://gitlab.com/datadrivendiscovery/jpl-primitives.git"
]
},
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@e75fecf908ed5c5fd7285cdc25eadffd3e2f3f7b#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.layer.subtract.KerasWrap",
......@@ -261,5 +261,5 @@
},
"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\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": "5e9167480e0cd0aeea22f3f92f556b7785c24f48e73244ca3609e9d59437eb60"
"digest": "8333e7351e0c4c4f599db95681ed41973079cb0524ba74c7a1e4a94b1b4718b6"
}
{
"problem": "124_120_mnist_problem",
"full_inputs": ["124_120_mnist_dataset"],
"train_inputs": ["124_120_mnist_dataset"],
"test_inputs": ["124_120_mnist_dataset"],
"score_inputs": ["124_120_mnist_dataset"]
}
\ No newline at end of file
context: TESTING
created: "2019-04-26T01:09:44.343543Z"
id: 32418dc0-a64d-4cc6-af3f-671a828a22e3
inputs:
- name: inputs
outputs:
- data: steps.9.produce
name: "output predictions"
schema: "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json"
steps:
- arguments:
inputs:
data: inputs.0
type: CONTAINER
outputs:
- id: produce
primitive:
id: f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e
name: "Denormalize datasets"
python_path: d3m.primitives.data_transformation.denormalize.Common
version: 0.2.0
type: PRIMITIVE
- arguments:
inputs:
data: steps.0.produce
type: CONTAINER
outputs:
- id: produce
primitive:
id: 4b42ce1e-9b98-4a25-b68e-fad13311eb65
name: "Extract a DataFrame from a Dataset"
python_path: d3m.primitives.data_transformation.dataset_to_dataframe.Common
version: 0.3.0
type: PRIMITIVE
- arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
- id: produce
primitive:
id: 8f2e51e8-da59-456d-ae29-53912b2b9f3d
name: "Columns image reader"
python_path: d3m.primitives.data_preprocessing.image_reader.DataFrameCommon
version: 0.2.0
type: PRIMITIVE
- type: PRIMITIVE
primitive:
id: e8acc97a-7868-427e-b022-e2aa51116d19
version: 0.1.0
python_path: d3m.primitives.layer.flatten.KerasWrap
name: flatten
- 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
- 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
- type: PRIMITIVE
hyperparams:
units:
data: 100
type: VALUE
previous_layer:
data: 3
type: PRIMITIVE
primitive:
id: eb6a13fd-c3e6-4407-a15f-280905e6243e
version: 0.1.0
python_path: d3m.primitives.layer.dense.KerasWrap
name: dense
- type: PRIMITIVE
hyperparams:
units:
data: 10
type: VALUE
previous_layer:
data: 6
type: PRIMITIVE
primitive:
id: eb6a13fd-c3e6-4407-a15f-280905e6243e
version: 0.1.0
python_path: d3m.primitives.layer.dense.KerasWrap
name: dense
- arguments:
inputs:
data: steps.2.produce
type: CONTAINER
outputs:
data: steps.2.produce
type: CONTAINER
hyperparams:
return_result:
data: replace
type: VALUE
loss:
data: 5
type: PRIMITIVE
model_type:
data: classification
type: VALUE
network_last_layer:
data: 7
type: PRIMITIVE
outputs:
- id: produce
primitive:
id: f8b81d1a-3e22-4edf-aa99-15bcbe827954
name: model
python_path: d3m.primitives.learner.model.KerasWrap
version: 0.2.0
type: PRIMITIVE
- arguments:
inputs:
data: steps.8.produce
type: CONTAINER
reference:
data: steps.1.produce
type: CONTAINER
outputs:
- id: produce
primitive:
id: 8d38b340-f83f-4877-baaa-162f8e551736
name: "Construct pipeline predictions output"
python_path: d3m.primitives.data_transformation.construct_predictions.DataFrameCommon
version: 0.3.0
type: PRIMITIVE
......@@ -17,7 +17,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@1e4c7536f4241ded731113c1a6da26b48a16a2fc#egg=jpl_primitives"</