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Change the example to be independent of the model_definition used.
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bogdan-XT
@@ -97,51 +97,56 @@ Select the devices and click on the **DEPLOY MODEL** button.
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* The pycom module provides two functions for model interaction: `pycom.ml_new_model()` and `pycom.ml_run_model()`. Below is a very simple example:
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```python
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from math import ceil
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from math import sin
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import json
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import pycom
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# This is just an example. In a real application, the input
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# This is just a dummy example. In a real application, the input
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# data should be collected from the accelerometer.
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# In order for this model to run with the data_l defined here, in
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# the model_definition.json, we must have: window_size_ms = 2000
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# and sampling_frequency=62.5! If the model used has other
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# settings please update the data_l list accordingly. The data_l
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# list should contain only one window of data, on three axes!
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# A window which contains a gesture. It is a list with 126 * 3 = 378
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# entries. This is because in the model_definition.json the
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# window_size_ms = 2000 and sampling_frequency = 62.5, so there are
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# 62.5*2 + 1 = 126 samples in a window. The data is in the next
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# format: acc_x, acc_y, acc_z, acc_x, ...
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data_l = [-0.9337, 0.0575, 11.8405, -0.7422, -0.1006, 10.6195, -0.1532, 0.6129, 10.0353, -0.4357, 0.7135, 7.6938, -0.4357, 0.7135, 7.6938, -0.4405, 0.5315, 6.3434, -0.2346, 0.4788, 4.5191, 0.1772, 0.6177, 3.705, 0.4597, 0.5219, 4.7202, 0.8619, 1.2258, 6.1711, 1.1013, 2.2936, 6.1567, 0.9002, 2.2362, 4.0642, 0.9002, 2.2362, 4.0642, 0.2921, 1.7573, 1.5407, 0.656, 1.4748, -0.1448, 0.8523, 1.3408, -0.3938, 1.2067, 1.3551, 0.9373, 1.403, 1.9106, 2.7809, 1.0056, 1.9776, 3.4081, 1.0056, 1.9776, 3.4081, 1.1396, 1.5419, 2.8766, 1.0439, 1.1684, 3.1831, 1.1971, 1.3743, 3.5853, 0.9816, 1.6616, 3.6332, 0.249, 1.8387, 3.5949, 0.4884, 1.8675, 4.203, 0.5698, 1.7382, 5.2948, 0.3543, 1.901, 5.8646, 0.3687, 1.9249, 5.7928, 0.3687, 1.9249, 5.7928, 0.5459, 1.4078, 5.357, 0.4741, 0.4549, 5.3762, 0.5842, 0.723, 6.8414, 0.3639, 1.1444, 7.6363, 0.3639, 1.2019, 8.8334, 0.4884, 0.565, 9.341, 0.723, 0.6033, 10.088, 0.407, 0.6752, 10.1359, -0.431, 0.7374, 10.1023, -0.3112, 0.68, 11.9459, 0.249, 0.5842, 13.4494, 0.0479, 0.7183, 13.21, -0.972, 0.8763, 12.5396, -0.814, 0.6656, 13.8181, -0.6321, 0.7039, 15.451, -1.221, 0.565, 15.2259, -1.4317, 0.5028, 15.1972, -1.7526, 0.8811, 16.356, -2.2553, 0.9242, 17.5339, -2.3559, 1.5802, 18.7646, -2.5331, 1.5371, 19.2099, -2.7054, 1.6616, 19.5882, -2.7916, 1.6185, 19.8084, -2.7916, 1.6185, 19.8084, -2.5762, 1.5993, 20.0383, -2.5762, 1.5993, 20.0383, -2.1117, 0.9529, 18.9321, -1.81, 0.4645, 17.4765, -1.6137, 0.2873, 17.1365, -1.2402, 0.1053, 16.3895, -1.0439, 0.0479, 15.4414, -0.7087, -0.1006, 14.613, -0.5124, -0.1101, 14.273, -0.6752, 0.5028, 13.5787, -0.4741, 0.6943, 12.9036, -0.3926, 0.7422, 11.8453, -0.3926, 0.7422, 11.8453, -0.1006, 0.5219, 10.4088, 0.3926, 0.5459, 11.9459, 0.5555, 0.7422, 11.491, 0.4693, 1.2402, 8.5317, -0.2921, 0.8188, 4.9979, -0.2921, 0.8188, 4.9979, 0.1006, 0.7757, 5.0985, 0.8523, 1.0918, 7.9332, 0.8236, 2.4086, 9.1447, 0.2921, 2.5953, 7.2532, 0.431, 2.2122, 5.9891, 1.1109, 1.2258, 3.8439, 1.1109, 1.2258, 3.8439, 0.838, 0.431, 1.5838, 1.2929, 0.5986, 1.1432, 1.3503, 0.838, 1.7178, 1.336, 1.7813, 3.0969, 0.9194, 2.4229, 3.8678, 0.7901, 2.4852, 4.2365, 0.7087, 2.3894, 4.2653, 0.8763, 1.6999, 4.045, 0.9912, 1.4413, 3.7816, 0.8092, 1.5993, 4.1312, 0.7757, 1.8531, 4.8638, 0.7374, 1.8004, 5.2086, 0.6512, 1.8244, 5.1415, 0.6177, 2.2362, 5.6204, 0.2203, 2.2745, 5.5869, -0.2346, 1.7382, 5.0218, 0.1724, 1.5083, 5.1846, 0.7949, 1.5802, 6.7935, 0.2251, 2.5091, 7.8326, -0.2346, 2.4038, 7.7129, -0.2729, 1.8196, 7.4974, 0.2921, 1.5802, 8.0385, -0.2586, 1.6712, 8.6706, -0.3065, 1.7382, 9.0633, -0.3065, 1.7382, 9.0633, -0.0144, 1.6233, 10.1885, -0.182, 1.0056, 10.8302, -0.3017, 0.9337, 10.5285, -0.5267, 0.9194, 10.337, -0.6656, 0.7805, 11.2803, -0.1389, 0.1484, 12.5588, -0.067, 0.2586, 12.9658, -0.0527, 0.4214, 13.6601, -0.7901, 0.8332, 13.3824, -0.5507, 0.3735, 12.2236, -0.7278, 0.1053, 13.8947, -0.5602, 0.0718, 14.5125, -0.9816, 0.5938, 15.0344, -1.3791, 0.407, 15.2403, -1.3503, 0.3112, 16.2746, -1.494, 0.407, 16.6146, -1.494, 0.407, 16.6146, -1.992, 0.6512, 17.2466, -2.3367, 1.3216, 18.4725, -2.1883, 1.5897, 18.9178, -2.4229, 1.6712, 18.5634]
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# Read deployed model.
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with open('flash/model_definition.json') as file:
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model_str = file.read()
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model_dict = json.loads(model_str)
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# Read labels.
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for block in model_dict['model']['blocks']:
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if block['block_type'] == 'nn_block':
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output_labels = block['trained_nn_model']['label_to_id']
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def new_model():
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def new_model(model_str):
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"""Instantiate deployed model."""
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return pycom.ml_new_model(model_str)
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def run_model():
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def run_model(window_data):
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"""Run model to classify data."""
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result = pycom.ml_run_model(data_l)['NN']
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result = pycom.ml_run_model(window_data)['NN']
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# Map probabilities to labels and print the result.
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for (label, index) in output_labels.items():
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print('{}: {:.2}%'.format(label, result[index] * 100))
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print('Results:')
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for (label, index) in nn_block['trained_nn_model']['label_to_id'].items():
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print(' {}: {:.2}%'.format(label, result[index] * 100))
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if new_model():
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print('Model succesfully created')
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run_model()
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# Read deployed model.
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with open('flash/model_definition.json') as file:
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# Parse the model_definition.
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model_str = file.read()
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model_dict = json.loads(model_str)
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# Read blocks.
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for block in model_dict['model']['blocks']:
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if block['block_type'] == 'pre_processing_block':
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pp_block = block
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if block['block_type'] == 'nn_block':
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nn_block = block
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# Compute the number of samples in a moving window. A sample
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# consists from three values, coresponding to the X, Y, Z axes.
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number_of_samples = ceil((pp_block['window_size_ms'] / 1000) * pp_block['sampling_frequency']) + 1
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# Generate a dummy moving window.
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window_data = []
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for i in range(number_of_samples):
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value = sin(i * 2 * 3.141592 / number_of_samples)
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window_data.append(value) # x_value
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window_data.append(value) # y_value
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window_data.append(value) # z_value
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if new_model(model_str):
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print('Model succesfully created')
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run_model(window_data)
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```
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* And an example in which data is real-time collected from the accelerometer:
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