Python cntk.classification_error() Examples

The following are 23 code examples of cntk.classification_error(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cntk , or try the search function .
Example #1
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #2
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #3
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def classification_error(output, target, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #4
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #5
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #6
Source File: language_understanding.py    From nlp-services with MIT License 5 votes vote down vote up 
def create_criterion_function(model):
        labels = C.placeholder(name='labels')
        ce = C.cross_entropy_with_softmax(model, labels)
        errs = C.classification_error(model, labels)
        return C.combine([ce, errs])  # (features, labels) -> (loss, metric)
Example #7
Source File: language_understanding.py    From nlp-services with MIT License 5 votes vote down vote up 
def create_criterion_function_preferred(model, labels):
        ce = C.cross_entropy_with_softmax(model, labels)
        errs = C.classification_error(model, labels)
        return ce, errs  # (model, labels) -> (loss, error metric)
Example #8
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #9
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #10
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #11
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #12
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #13
Source File: test_ops_compoud.py    From ngraph-python with Apache License 2.0 5 votes vote down vote up 
def _create_model_and_execute_test(params):
    # Create CNTK model
    input_var = C.input_variable(params['input_dim'], np.float32)
    params['input_var'] = input_var
    params['act_fun'] = C.layers.blocks.identity
    params['init_fun'] = C.glorot_uniform()

    model = params['create_model'](params)

    label_var = C.input_variable((params['label_dim']), np.float32)
    loss = C.cross_entropy_with_softmax(model, label_var)
    eval_error = C.classification_error(model, label_var)

    lr_schedule = C.learning_rate_schedule(0.05, C.UnitType.minibatch)
    learner = C.sgd(model.parameters, lr_schedule)
    trainer = C.Trainer(model, (loss, eval_error), [learner])

    input_value, label_value = _generate_random_sample(
        params['batch_size'],
        params['input_dim'],
        params['label_dim']
    )

    # Import to ngraph
    ng_loss, placeholders = CNTKImporter(batch_size=params['batch_size']).import_model(loss)
    parallel_update = CommonSGDOptimizer(0.05).minimize(ng_loss, ng_loss.variables())

    transformer = ng.transformers.make_transformer()
    update_fun = transformer.computation([ng_loss, parallel_update], *placeholders)

    # Execute on CNTK
    trainer.train_minibatch({input_var: input_value, label_var: label_value})
    cntk_ret = trainer.previous_minibatch_loss_average

    # Execute on ngraph
    input_value = np.moveaxis(input_value, 0, -1)
    label_value = np.moveaxis(label_value, 0, -1)
    ng_ret = update_fun(input_value, label_value)[0]

    return cntk_ret, ng_ret
Example #14
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #15
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #16
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #17
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #18
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #19
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #20
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #21
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())
Example #22
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def in_top_k(predictions, targets, k):
    _targets = C.one_hot(targets, predictions.shape[-1])
    result = C.classification_error(predictions, _targets, topN=k)
    return 1 - C.reshape(result, shape=())
Example #23
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def classification_error(target, output, axis=-1):
    return C.ops.reduce_mean(
        C.equal(
            argmax(
                output,
                axis=-1),
            argmax(
                target,
                axis=-1)),
        axis=C.Axis.all_axes())