Repository revision
19:f22a9297440f

Repository 'keras_model_config'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config

Create a deep learning model architecture tool metadata
Miscellaneous
using Keras
keras_model_config
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.10.0
1.0.10.0
echo "1.0.10.0"
True
Version lineage of this tool (guids ordered most recent to oldest)
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.11.0
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.10.0 (this tool)
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/0.5.0
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/0.4.2
toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/0.4.0
keras_model_config
Requirements (dependencies defined in the <requirements> tag set)
name version type
python 3.9 package
galaxy-ml 0.10.0 package
Functional tests
name inputs outputs required files
Test-1 model_selection|input_shape: (32, 32, 3)
model_selection|layers_0|layer_selection|filters: 32
model_selection|layers_0|layer_selection|kernel_size: (3, 3)
model_selection|layers_0|layer_selection|activation: relu
model_selection|layers_0|layer_selection|kwargs: padding='same'
model_selection|layers_0|layer_selection|layer_type: Conv2D
model_selection|layers_1|layer_selection|pool_size: (2, 2)
model_selection|layers_1|layer_selection|layer_type: MaxPooling2D
model_selection|layers_2|layer_selection|rate: 0.25
model_selection|layers_2|layer_selection|layer_type: Dropout
model_selection|layers_3|layer_selection|filters: 64
model_selection|layers_3|layer_selection|kernel_size: (3, 3)
model_selection|layers_3|layer_selection|activation: relu
model_selection|layers_3|layer_selection|kwargs: padding='same'
model_selection|layers_3|layer_selection|layer_type: Conv2D
model_selection|layers_4|layer_selection|pool_size: (2, 2)
model_selection|layers_4|layer_selection|layer_type: MaxPooling2D
model_selection|layers_5|layer_selection|rate: 0.25
model_selection|layers_5|layer_selection|layer_type: Dropout
model_selection|layers_6|layer_selection|layer_type: Flatten
model_selection|layers_7|layer_selection|units: 512
model_selection|layers_7|layer_selection|activation: relu
model_selection|layers_7|layer_selection|layer_type: Dense
model_selection|layers_8|layer_selection|rate: 0.5
model_selection|layers_8|layer_selection|layer_type: Dropout
model_selection|layers_9|layer_selection|units: 10
model_selection|layers_9|layer_selection|activation: softmax
model_selection|layers_9|layer_selection|layer_type: Dense
model_selection|model_type: sequential
name: value
value
Test-2 model_selection|input_shape: (784, )
model_selection|layers_0|layer_selection|units: 32
model_selection|layers_0|layer_selection|layer_type: Dense
model_selection|layers_1|layer_selection|activation: relu
model_selection|layers_1|layer_selection|layer_type: Activation
model_selection|layers_2|layer_selection|units: 10
model_selection|layers_2|layer_selection|layer_type: Dense
model_selection|layers_3|layer_selection|activation: softmax
model_selection|layers_3|layer_selection|layer_type: Activation
model_selection|model_type: sequential
name: value
value
Test-3 model_selection|layers_0|layer_selection|shape: (100, )
model_selection|layers_0|layer_selection|name: main_input
model_selection|layers_0|layer_selection|dtype: int32
model_selection|layers_0|layer_selection|layer_type: Input
model_selection|layers_1|layer_selection|input_dim: 10000
model_selection|layers_1|layer_selection|output_dim: 512
model_selection|layers_1|layer_selection|kwargs: input_length=100
model_selection|layers_1|layer_selection|inbound_nodes: 1
model_selection|layers_1|layer_selection|layer_type: Embedding
model_selection|layers_2|layer_selection|units: 32
model_selection|layers_2|layer_selection|inbound_nodes: 2
model_selection|layers_2|layer_selection|layer_type: LSTM
model_selection|layers_3|layer_selection|units: 1
model_selection|layers_3|layer_selection|activation: sigmoid
model_selection|layers_3|layer_selection|inbound_nodes: 3
model_selection|layers_3|layer_selection|layer_type: Dense
model_selection|layers_4|layer_selection|shape: (5, )
model_selection|layers_4|layer_selection|name: aux_input
model_selection|layers_4|layer_selection|dtype: float32
model_selection|layers_4|layer_selection|layer_type: Input
model_selection|layers_5|layer_selection|merging_layers: [4, 5]
model_selection|layers_5|layer_selection|layer_type: Concatenate
model_selection|layers_6|layer_selection|units: 64
model_selection|layers_6|layer_selection|activation: relu
model_selection|layers_6|layer_selection|inbound_nodes: 6
model_selection|layers_6|layer_selection|layer_type: Dense
model_selection|layers_7|layer_selection|units: 64
model_selection|layers_7|layer_selection|activation: relu
model_selection|layers_7|layer_selection|inbound_nodes: 7
model_selection|layers_7|layer_selection|layer_type: Dense
model_selection|layers_8|layer_selection|units: 64
model_selection|layers_8|layer_selection|activation: relu
model_selection|layers_8|layer_selection|inbound_nodes: 8
model_selection|layers_8|layer_selection|layer_type: Dense
model_selection|layers_9|layer_selection|units: 1
model_selection|layers_9|layer_selection|activation: sigmoid
model_selection|layers_9|layer_selection|inbound_nodes: 9
model_selection|layers_9|layer_selection|layer_type: Dense
model_selection|input_layers: [1, 5]
model_selection|output_layers: [4, 10]
model_selection|model_type: functional
name: value
value
Test-4 model_selection|input_shape: (17, )
model_selection|layers_0|layer_selection|units: 32
model_selection|layers_0|layer_selection|layer_type: Dense
model_selection|layers_1|layer_selection|activation: linear
model_selection|layers_1|layer_selection|layer_type: Activation
model_selection|layers_2|layer_selection|units: 1
model_selection|layers_2|layer_selection|layer_type: Dense
model_selection|layers_3|layer_selection|activation: linear
model_selection|layers_3|layer_selection|layer_type: Activation
model_selection|model_type: sequential
name: value
value
Test-5 model_selection|input_shape: (17, )
model_selection|layers_0|layer_selection|units: 100
model_selection|layers_0|layer_selection|layer_type: Dense
model_selection|layers_1|layer_selection|rate: 0.1
model_selection|layers_1|layer_selection|layer_type: Dropout
model_selection|layers_2|layer_selection|units: 1
model_selection|layers_2|layer_selection|layer_type: Dense
model_selection|model_type: sequential
name: value
value