# file: loss_softmax_entr_layer_dense_batch.py
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#
# !  Content:
# !    Python example of forward and backward softmax cross-entropy layer usage
# !
# !*****************************************************************************

#
## <a name="DAAL-EXAMPLE-PY-LOSS_SOFTMAX_ENTR_LAYER_DENSE_BATCH"></a>
## \example loss_softmax_entr_layer_dense_batch.py
#

import os
import sys

from daal.algorithms.neural_networks import layers
from daal.algorithms.neural_networks.layers import loss
from daal.algorithms.neural_networks.layers.loss import softmax_cross

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printTensor, readTensorFromCSV

# Input data set parameters
datasetName = os.path.join("..", "data", "batch", "softmax_cross_entropy_layer.csv")
datasetGroundTruthName = os.path.join("..", "data", "batch", "softmax_cross_entropy_layer_ground_truth.csv")

if __name__ == "__main__":

    # Retrieve the input data
    tensorData = readTensorFromCSV(datasetName)
    groundTruth = readTensorFromCSV(datasetGroundTruthName, True)

    # Create an algorithm to compute forward softmax cross-entropy layer results using default method
    softmaxCrossLayerForward = loss.softmax_cross.forward.Batch(method=loss.softmax_cross.defaultDense)

    # Set input objects for the forward softmax_cross layer
    softmaxCrossLayerForward.input.setInput(layers.forward.data, tensorData)
    softmaxCrossLayerForward.input.setInput(loss.forward.groundTruth, groundTruth)

    # Compute forward softmax_cross layer results
    forwardResult = softmaxCrossLayerForward.compute()

    # Print the results of the forward softmax_cross layer
    printTensor(forwardResult.getResult(layers.forward.value), "Forward softmax cross-entropy layer result (first 5 rows):", 5)
    printTensor(forwardResult.getLayerData(loss.softmax_cross.auxProbabilities), "Softmax Cross-Entropy layer probabilities estimations (first 5 rows):", 5)
    printTensor(forwardResult.getLayerData(loss.softmax_cross.auxGroundTruth), "Softmax Cross-Entropy layer ground truth (first 5 rows):", 5)

    # Create an algorithm to compute backward softmax_cross layer results using default method
    softmaxCrossLayerBackward = softmax_cross.backward.Batch(method=loss.softmax_cross.defaultDense)

    # Set input objects for the backward softmax_cross layer
    softmaxCrossLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))

    # Compute backward softmax_cross layer results
    backwardResult = softmaxCrossLayerBackward.compute()

    # Print the results of the backward softmax_cross layer
    printTensor(backwardResult.getResult(layers.backward.gradient), "Backward softmax cross-entropy layer result (first 5 rows):", 5)
