Artificial Neural Network (DL_ANN)
This function creates and uses a model consisting of layers of interconnected nodes (neurons) that process input data and produce output predictions.
An artificial neural network (ANN) is a type of machine learning model inspired by the structure and function of biological neurons in the human brain. It consists of layers of interconnected nodes (neurons) that process input data and produce output predictions. Each neuron takes in one or more inputs, applies a mathematical function to them, and passes the result to the next layer of neurons. By adjusting the weights and biases of the connections between neurons during training, the network can learn to make accurate predictions on new data. ANNs are used for a wide variety of tasks, including image and speech recognition, natural language processing, and predictive modeling.
Sample Request
Build a sequential ANN model for Binary Classification
Building a Sequential Artificial Neural Network
Sequential ANN Construction
POST
https://autogon.ai/api/v1/engine/start
Request Body
project_id*
int
The id
of the current project
block_id*
int
The id
of the current block
function_code*
string
The function code for current block
parent_id*
int
The id
of the previous block
args*
object
Block arguments
layer_list*
list
List of layers for the Artificial Neural Network
type*
string
Type of layer to add
units*
int
Dimensionality of the output space of the layer
activation
string
Activation function applied to the layer's output. Default: "relu".
filters
int
Number of filters or output channels in the convolutional layer. Default: 32.
kernel_size
array
Size of the convolutional kernel. Default: [3, 3].
padding
string
Padding scheme for the layer. Default: "valid".
pool_size
array
Size of the pooling window. Default: [2, 2].
rate
float
Fraction of input units to drop during training (0-1). Default: 0.5.
input_dim
int
Size of the input vocabulary. Default: 1000.
output_dim
int
Dimensionality of the dense embedding. Default: 64.
return_sequences
bool
Whether to return the full sequence or only the last output. Default: false.
size
array
The upsampling factors for rows and columns.
Default: [2, 2].
Sample Request
Compile and train the pre-built ANN model, using passed-in Hyper Parameters
Training an Artificial Neural Network
ANN Training
POST
https://autogon.ai/api/v1
Request Body
project_id*
int
The id
of the current project
hyp_params*
object
hyper parameters for model compilation and training
parent_id*
int
The id
of the previous block
block_id*
int
The id
of the current block
function_code*
The function code for current block
args*
object
Block arguments
model_name*
String
The name the model would be saved with
optimizer
String
Optimization function to be used e.g: "adam"
loss*
String
Loss function to be used e.g: "binary_crossentropy"
metrics
list
Evaluation metrics used to judge the performance of the model
batch_size
String
Number of samples that are processed by the model during each training iteration
epochs*
String
Number of iterations through the dataset
add_dim
bool
Sets whether an extra dimension should be added to x
train data
autoencoder
bool
Specifies whether to use x data as y data
Sample Request
Evaluate the accuracy and losses of a trained artificial neural network.
Evaluating an Artificial Neural Network
ANN Evaluating
POST
Request Body
project_id*
int
The id
of the current project
parent_id*
int
The id
of the previous block
block_id*
int
The id
of the current block
function_code*
String
Function code for the current block
args*
object
Block arguments
hyp_params*
object
hyper parameters for model evaluation
batch_size
int
Number of samples that are processed by the model during evaluation
Sample Request
Make predictions with the trained ANN model.
Predicting with an Artificial Neural Network
ANN Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
test_data
String
Input data for prediction Defaults to x_test_url
project_id*
int
ID of the current project
block_id*
int
ID of the current block
parent_id*
int
ID of the previous block
function_code*
String
Function code for the current block
args*
object
Block arguments
add_dim
bool
Sets whether an extra dimension should be added to x
data
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