Merge branch 'blis/ops14todo' of https://github.com/Microsoft/CNTK into blis/ops14todo
This commit is contained in:
Willi Richert
Коммит
4945bb8504
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@ -4,75 +4,99 @@
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# for full license information.
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# ==============================================================================
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# TODO: re-write the example using the new facade
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"""
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MNIST Example, one hidden layer neural network
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MNIST Example, one hidden layer neural network using training and testing data
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generated through `uci_to_cntk_text_format_converter.py
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<https://github.com/Microsoft/CNTK/blob/master/Source/Readers/CNTKTextFormatReader/uci_to_cntk_text_format_converter.py>`_
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to convert it to the CNTKTextFormatReader format.
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"""
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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..'))
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from cntk import *
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import numpy as np
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import cntk as C
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def add_dnn_sigmoid_layer(in_dim, out_dim, x, param_scale):
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W = LearnableParameter(out_dim, in_dim, initValueScale=param_scale)
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b = LearnableParameter(out_dim, 1, initValueScale=param_scale)
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t = Times(W, x)
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z = Plus(t, b)
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return Sigmoid(z)
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W = C.parameter((out_dim, in_dim), init_value_scale=param_scale)
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b = C.parameter((out_dim, 1), init_value_scale=param_scale)
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t = C.times(W, x)
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z = C.plus(t, b)
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return C.sigmoid(z)
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def add_dnn_layer(in_dim, out_dim, x, param_scale):
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W = LearnableParameter(out_dim, in_dim, initValueScale=param_scale)
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b = LearnableParameter(out_dim, 1, initValueScale=param_scale)
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t = Times(W, x)
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return Plus(t, b)
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W = C.parameter((out_dim, in_dim), init_value_scale=param_scale)
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b = C.parameter((out_dim, 1), init_value_scale=param_scale)
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t = C.times(W, x)
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return C.plus(t, b)
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if (__name__ == "__main__"):
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def train_eval_mnist_onelayer_from_file(criterion_name=None, eval_name=None):
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# Network definition
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feat_dim = 784
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label_dim = 10
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hidden_dim = 200
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cur_dir = os.path.dirname(__file__)
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training_filename = os.path.join("Data", "Train-28x28.txt")
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test_filename = os.path.join("Data", "Test-28x28.txt")
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training_filename = os.path.join(cur_dir, "Data", "Train-28x28_text.txt")
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test_filename = os.path.join(cur_dir, "Data", "Test-28x28_text.txt")
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features = Input(feat_dim)
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features = C.input(feat_dim)
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features.name = 'features'
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feat_scale = Constant(0.00390625)
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feats_scaled = Scale(feat_scale, features)
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feat_scale = C.constant(0.00390625)
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feats_scaled = C.element_times(features, feat_scale)
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labels = Input(label_dim)
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labels = C.input(label_dim)
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labels.tag = 'label'
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labels.name = 'labels'
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f_reader = UCIFastReader(training_filename, 1, feat_dim)
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l_reader = UCIFastReader(training_filename, 0, 1, label_dim,
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os.path.join("Data", "labelsmap.txt"))
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f_reader_t = UCIFastReader(test_filename, 1, feat_dim)
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l_reader_t = UCIFastReader(test_filename, 0, 1, label_dim,
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os.path.join("Data", "labelsmap.txt"))
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traning_reader = C.CNTKTextFormatReader(training_filename)
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test_reader = C.CNTKTextFormatReader(test_filename)
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h1 = add_dnn_sigmoid_layer(feat_dim, hidden_dim, feats_scaled, 1)
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out = add_dnn_layer(hidden_dim, label_dim, h1, 1)
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out.tag = 'output'
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ec = CrossEntropyWithSoftmax(labels, out)
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ec = C.cross_entropy_with_softmax(labels, out)
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ec.name = criterion_name
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ec.tag = 'criterion'
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eval = C.ops.square_error(labels, out)
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eval.name = eval_name
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eval.tag = 'eval'
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# Specify the training parameters (settings are scaled down)
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my_sgd = SGDParams(epoch_size=600, minibatch_size=32,
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learning_ratesPerMB=0.1, max_epochs=5, momentum_per_mb=0)
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my_sgd = C.SGDParams(epoch_size=600, minibatch_size=32,
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learning_rates_per_mb=0.1, max_epochs=5, momentum_per_mb=0)
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# Create a context or re-use if already there
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with LocalExecutionContext('mnist_one_layer', clean_up=True) as ctx:
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with C.LocalExecutionContext('mnist_one_layer', clean_up=True) as ctx:
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# CNTK actions
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ctx.train(ec, my_sgd, {features: f_reader, labels: l_reader})
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ctx.write({features: f_reader_t, labels: l_reader_t})
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print(ctx.test({features: f_reader_t, labels: l_reader_t}))
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ctx.train(
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root_nodes=[ec, eval],
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training_params=my_sgd,
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input_map=traning_reader.map(labels, alias='labels', dim=label_dim).map(features, alias='features', dim=feat_dim))
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result = ctx.test(
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root_nodes=[ec, eval],
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input_map=test_reader.map(labels, alias='labels', dim=label_dim).map(features, alias='features', dim=feat_dim))
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return result
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def test_mnist_onelayer_from_file():
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result = train_eval_mnist_onelayer_from_file('crit_node', 'eval_node')
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TOLERANCE_ABSOLUTE = 1E-06
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assert result['SamplesSeen'] == 10000
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assert np.allclose(result['Perplexity'], 7.6323031, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['crit_node'], 2.0323896, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['eval_node'], 1.9882504, atol=TOLERANCE_ABSOLUTE)
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if __name__ == "__main__":
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print(train_eval_mnist_onelayer_from_file('crit_node', 'eval_node'))
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@ -518,8 +518,14 @@ def future_value(dims, x, time_step=1, default_hidden_activation=0.1, name=None)
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value is returned which is 0.1 by default.
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Example:
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>>> future_value(0, [[1, 2], [3, 4], [5,6]], 1, 0.5)
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# [[3, 4], [5, 6], [0.5, 0.5]]
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>>> data = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
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>>> t = C.dynamic_axis(name='t')
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>>> x = C.input_numpy([data], dynamic_axis=t)
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>>> with C.LocalExecutionContext('future_value') as ctx:
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... print(ctx.eval(C.future_value(0, x)))
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[array([[ 5. , 6. , 7. , 8. ],
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[ 9. , 10. , 11. , 12. ],
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[ 0.1, 0.1, 0.1, 0.1]])]
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Args:
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dims: dimensions of the input `x`
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@ -544,8 +550,14 @@ def past_value(dims, x, time_step=1, default_hidden_activation=0.1, name=None):
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value is returned which is 0.1 by default.
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Example:
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>>> past_value(0, [[1, 2], [3, 4], [5,6]], 1, 0.5)
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# [[0.5, 0.5], [1, 2], [3, 4]]
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>>> data = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
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>>> t = C.dynamic_axis(name='t')
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>>> x = C.input_numpy([data], dynamic_axis=t)
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>>> with C.LocalExecutionContext('past_value') as ctx:
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... print(ctx.eval(C.past_value(0, x)))
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[array([[ 0.1, 0.1, 0.1, 0.1],
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[ 1. , 2. , 3. , 4. ],
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[ 5. , 6. , 7. , 8. ]])]
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Args:
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dims: dimensions of the input `x`
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@ -755,6 +767,9 @@ def reconcile_dynamic_axis(data_input, layout_input, name=None):
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of `layout_input`. It allows these two tensors to be properly compared using, e.g.
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a criterion node.
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Example:
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See Examples/LSTM/seqcla.py for a use of :func:`cntk.ops.reconcile_dynamic_axis`.
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Args:
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data_input: the tensor to have its dynamic axis layout adapted
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layout_input: the tensor layout to use for adapting `data_input`s layout
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@ -281,3 +281,6 @@ the criterion node that adds a softmax and then implements the cross entropy los
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we add the criterion node, however, we call :func:`cntk.ops.reconcile_dynamic_axis` which will ensure
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that the minibatch layout for the labels and the data with dynamic axes is compatible.
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For the full explanation of how ``lstm_layer()`` is defined, please see the full example in the
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Examples section.
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