Update Getting started section of the Python API docs
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Jorge Tavares
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@ -32,15 +32,16 @@ Installing the Python module
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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#. Go to ``<cntkpath>/contrib/Python`` and run ``python setup.py install``
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#. Set up the environment variable ``CNTK_EXECUTABLE_PATH`` to point to the
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CNTK executable
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CNTK executable. Make sure the executable is also included
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#. Enjoy Python's ease of use with CNTK's speed::
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>>> import cntk as cn
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>>> cn.__version__
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>>> import cntk as C
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>>> C.__version__
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1.4
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>>> with cn.Context('demo', clean_up=False) as ctx:
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... a = cn.constant([[1,2], [3,4]])
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... print(ctx.eval(a + [[10,20], [30, 40]]))
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>>> with C.LocalExecutionContext('demo', clean_up=False) as ctx:
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... a = C.constant([[1,2], [3,4]])
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... i = C.input_numpy([[[10,20], [30, 40]]])
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... print(ctx.eval(a + i))
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[[11.0, 22.0], [33.0, 44.0]]
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In this case, we have set ``clean_up=False`` so that you can now peek into the
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@ -71,37 +72,36 @@ explained::
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import cntk as C
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import numpy as np
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def simple_network():
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# 500 samples, 250-dimensional data
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N = 500
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d = 250
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# 500 samples, 250-dimensional data
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N = 500
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d = 250
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# create synthetic data using numpy
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X = np.random.randn(N, d)
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Y = np.random.randint(size=(N, 1), low=0, high=2)
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Y = np.hstack((Y, 1-Y))
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# create synthetic data using numpy
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X = np.random.randn(N, d)
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Y = np.random.randint(size=(N, 1), low=0, high=2)
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Y = np.hstack((Y, 1-Y))
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# set up the training data for CNTK
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x = C.input_numpy(X, has_dynamic_axis=False)
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y = C.input_numpy(Y, has_dynamic_axis=False)
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# set up the training data for CNTK
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x = C.input_numpy(X)
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y = C.input_numpy(Y)
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# define our network parameters: a weight tensor and a bias
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W = C.parameter((2, d))
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b = C.parameter((2, 1))
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# create a dense 'layer' by multiplying the weight tensor and
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# the features and adding the bias
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out = C.times(W, x) + b
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# define our network parameters: a weight tensor and a bias
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W = C.parameter((d, 2))
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b = C.parameter((1, 2))
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# setup the criterion node using cross entropy with softmax
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ce = C.cross_entropy_with_softmax(y, out, name='loss')
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ce.tag = 'criterion'
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# create a dense 'layer' by multiplying the weight tensor and
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# the features and adding the bias
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out = C.times(x, W) + b
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# define our SGD parameters and train!
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my_sgd = C.SGDParams(epoch_size=0, minibatch_size=25, learning_rates_per_mb=0.1, max_epochs=3)
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with C.LocalExecutionContext('logreg') as ctx:
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ctx.train(root_nodes=[ce], training_params=my_sgd)
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print(ctx.test(root_nodes=[ce]))
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# setup the criterion node using cross entropy with softmax
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ce = C.cross_entropy_with_softmax(y, out, name='loss')
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ce.tag = 'criterion'
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# define our SGD parameters and train!
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my_sgd = C.SGDParams(epoch_size=0, minibatch_size=25, learning_rates_per_mb=0.1, max_epochs=3)
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with C.LocalExecutionContext('logreg') as ctx:
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ctx.train(root_nodes=[ce], training_params=my_sgd)
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print(ctx.test(root_nodes=[ce]))
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In the example above, we first create a synthetic data set of 500 samples, each with a 2-dimensional
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@ -151,7 +151,7 @@ on the right.
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.. figure:: images/nn_layers.png
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:width: 600px
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:alt: NN Layers
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As is apparent from the figure above on the right, RNNs are the natural structure for
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dealing with sequences. This includes everything from text to music to video; anything
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where the current state is dependent on the previous state. While RNNs are indeed
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@ -169,7 +169,7 @@ concentrate on the central feature of the LSTM model: the `memory cell`.
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.. figure:: images/lstm_cell.png
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:width: 400px
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:alt: LSTM cell
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An LSTM cell.
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The LSTM cell is associated with three gates that control how information is stored /
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@ -208,48 +208,49 @@ sequence classification. We can think of the network as adding a series of layer
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We can define this network as follows in the CNTK Python API::
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import cntk as C
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import cntk as C
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def seqcla():
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# model
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num_labels = 5
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vocab = 2000
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embed_dim = 50
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# LSTM params
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input_dim = 50
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output_dim = 128
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cell_dim = 128
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def seqcla():
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# model
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num_labels = 5
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vocab = 2000
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embed_dim = 50
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t = C.dynamic_axis(name='t')
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# temporarily using cntk1 SpareInput because cntk2's input() will simply allow sparse as a parameter
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features = cntk1.SparseInput(vocab, dynamicAxis=t, name='features')
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labels = C.input(num_labels, name='labels')
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train_reader = C.CNTKTextFormatReader(train_file)
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# LSTM params
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input_dim = 50
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output_dim = 128
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cell_dim = 128
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# setup embedding matrix
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embedding = C.parameter((embed_dim, vocab),
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learning_rate_multiplier=0.0,
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init_from_file_path=embedding_file)
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t = C.dynamic_axis(name='t')
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# temporarily using cntk1 SparseInput because cntk2's input() will simply allow sparse as a parameter
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features = cntk1.SparseInput(vocab, dynamicAxis=t, name='features')
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labels = C.input(num_labels, name='labels')
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train_reader = C.CNTKTextFormatReader(train_file)
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# setup embedding matrix
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embedding = C.parameter((embed_dim, vocab),
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learning_rate_multiplier=0.0,
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init_from_file_path=embedding_file)
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# get the vector representing the word
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sequence = C.times(embedding, features, name='sequence')
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# add an LSTM layer
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L = lstm_layer(output_dim, cell_dim, sequence, input_dim)
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# add a dense layer on top
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w = C.parameter((num_labels, output_dim), name='w')
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b = C.parameter((num_labels), name='b')
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z = C.plus(C.times(w, L), b, name='z')
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z.tag = "output"
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# and reconcile the shared dynamic axis
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pred = C.reconcile_dynamic_axis(z, labels, name='pred')
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ce = C.cross_entropy_with_softmax(labels, pred)
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ce.tag = "criterion"
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# get the vector representing the word
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sequence = C.times(embedding, features, name='sequence')
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# add an LSTM layer
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L = lstm_layer(output_dim, cell_dim, sequence, input_dim)
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# add a dense layer on top
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w = C.parameter((num_labels, output_dim), name='w')
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b = C.parameter((num_labels), name='b')
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z = C.plus(C.times(w, L), b, name='z')
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z.tag = "output"
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# and reconcile the shared dynamic axis
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pred = C.reconcile_dynamic_axis(z, labels, name='pred')
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ce = C.cross_entropy_with_softmax(labels, pred)
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ce.tag = "criterion"
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Let's go through some of the intricacies of the above network definition. First, we define
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some parameters of the data and the network. We have 5 possible classes for the sequences;
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@ -282,7 +283,7 @@ 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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For the full explanation of how ``lstm_layer()`` is defined, please see the full example (`seqcla.py <https://github.com/Microsoft/CNTK/blob/master/contrib/Python/cntk/examples/LSTM/seqcla.py>`_) in the
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Examples section.
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How to pass Python data as train/test data
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