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<h1 id="observations.jsb_chorales">observations.jsb_chorales</h1> | ||
<pre class="python"><code>jsb_chorales(path)</code></pre> | ||
<p>Load the corpus of 382 four-part harmonized chorales from J.S. Bach. There are 202 chorales in major keys of which 121 were used for training and 81 used for testing; and 180 chorales in minor keys split as 108 and 72 respectively <span class="citation">(Allan & Williams, 2005)</span>. Data is loaded in the piano-roll representation <span class="citation">(Boulanger-Lewandowski, Bengio, & Vincent, 2012)</span>, i.e., a binary matrix specifying which notes occur at each time step.</p> | ||
<h4 id="args">Args:</h4> | ||
<ul> | ||
<li><b><code>path</code></b>: str. Path to directory which either stores file or otherwise file will be downloaded and extracted there. Filename is <code>JSB%20Chorales.pickle</code>.</li> | ||
</ul> | ||
<h4 id="returns">Returns:</h4> | ||
<p>list of <code>x_train, x_test, x_valid</code>, where each is a list of sequences. Each sequence is itself a list of time steps, and each time step is a list of the non-zero elements in the piano-roll at this instant (in MIDI note numbers, between 21 and 108 inclusive).</p> | ||
<div id="refs" class="references"> | ||
<div id="ref-allan2005harmonising"> | ||
<p>Allan, M., & Williams, C. (2005). Harmonising chorales by probabilistic inference. In <em>Neural information processing systems</em>.</p> | ||
</div> | ||
<div id="ref-boulanger2012modeling"> | ||
<p>Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In <em>International conference on machine learning</em>.</p> | ||
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<h1 id="observations.multi_mnist">observations.multi_mnist</h1> | ||
<pre class="python"><code>multi_mnist( | ||
path, | ||
max_digits=2, | ||
canvas_size=50, | ||
seed=42 | ||
)</code></pre> | ||
<p>Load the multiple MNIST data set <span class="citation">(Eslami et al., 2016)</span>. It modifies the original MNIST such that each image contains a number of non-overlapping random MNIST digits with equal probability.</p> | ||
<h4 id="args">Args:</h4> | ||
<ul> | ||
<li><b><code>path</code></b>: str. Path to directory which either stores file or otherwise file will be downloaded and extracted there. Filename is <code>'multi_mnist_{}_{}_{}.npz'.format(max_digits, canvas_size, seed)</code>.</li> | ||
<li><b><code>max_digits</code></b>: int, optional. Maximum number of non-overlapping MNIST digits per image to generate if not cached.</li> | ||
<li><b><code>canvas_size</code></b>: list of two int, optional. Width x height pixel size of generated images if not cached.</li> | ||
<li><b><code>seed</code></b>: int, optional. Random seed to generate the data set from MNIST if not cached.</li> | ||
</ul> | ||
<h4 id="returns">Returns:</h4> | ||
<p>Tuple of (np.ndarray of dtype uint8, list) <code>(x_train, y_train), (x_test, y_test)</code>. Each element in the y's is a np.ndarray of labels, one label for each digit in the image.</p> | ||
<div id="refs" class="references"> | ||
<div id="ref-eslami2016attend"> | ||
<p>Eslami, S. A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Hinton, G. E., & others. (2016). Attend, infer, repeat: Fast scene understanding with generative models. In <em>Neural information processing systems</em>.</p> | ||
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