deephyp.data.Iterator¶
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class
deephyp.data.Iterator(dataSamples, targets, batchSize=None)[source]¶ Class for iterating through data, to train the network.
Parameters: - dataSamples (np.array float) – Data to be input into the network. Shape [numSamples x numBands].
- targets (np.array int) – Network output target of each dataSample. For classification, these are the class labels, and it could be the dataSamples for autoencoders. Shape [numSamples x arbitrary]
- batchSize (int) – Number of dataSamples per batch
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dataSamples¶ Data to be input into the network. Shape [numSamples x numBands].
Type: np.array float
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targets¶ Network output target of each dataSample. For classification, these are the class labels, and it could be the dataSamples for autoencoders. Shape [numSamples x arbitrary]
Type: np.array int
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batchSize¶ Number of dataSamples per batch. If None - set to numSamples (i.e. whole dataset).
Type: int
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numSamples¶ The number of data samples.
Type: int
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currentBatch¶ A list of indexes specifying the data samples in the current batch. Shape [batchSize]
Type: int list
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get_batch(idx)[source]¶ Returns a specified set of samples and targets.
Parameters: idx (int list) – Indexes of samples (and targets) to return. Returns: 2-element tuple containing: - (np.array float) - Batch of data samples at [idx] indexes. Shape [length(idx) x numBands].
- (np.array int) - Batch of targets at [idx] indexes. Shape [length(idx) x arbitrary].
Return type: (tuple)
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next_batch()[source]¶ Return next batch of samples and targets (with batchSize number of samples). The currentBatch indexes are incremented. If end of dataset reached, the indexes wraps around to the beginning.
Returns: 2-element tuple containing: - (np.array float) - Batch of data samples at currentBatch indexes. Shape [batchSize x numBands].
- (np.array int) - Batch of targets at currentBatch indexes. Shape [batchSize x arbitrary].
Return type: (tuple)