43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import numpy as np
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from mlp.layers import BatchNormalizationLayer
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import argparse
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parser = argparse.ArgumentParser(description='Welcome to GAN-Shot-Learning script')
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parser.add_argument('--student_id', nargs="?", type=str, help='Your student id in the format "sxxxxxxx"')
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args = parser.parse_args()
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student_id = args.student_id
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def generate_inputs(student_id):
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student_number = student_id
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tests = np.arange(96).reshape((2, 3, 4, 4))
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tests[:, 0, :, :] = float(student_number[1:3]) / 10 - 5
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tests[:, :, 1, :] = float(student_number[3:5]) / 10 - 5
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tests[:, 2, :, :] = float(student_number[5:7]) / 10 - 5
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tests[0, 1, :, :] = float(student_number[7]) / 10 - 5
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return tests
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test_inputs = generate_inputs(student_id)
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test_inputs = np.reshape(test_inputs, newshape=(2, -1))
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test_grads_wrt_outputs = np.arange(-48, 48).reshape((2, -1))
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#produce BatchNorm Layer fprop and bprop
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activation_layer = BatchNormalizationLayer(input_dim=48)
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beta = np.array(48*[0.3])
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gamma = np.array(48*[0.8])
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activation_layer.params = [gamma, beta]
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BN_fprop = activation_layer.fprop(test_inputs)
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BN_bprop = activation_layer.bprop(
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test_inputs, BN_fprop, test_grads_wrt_outputs)
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BN_grads_wrt_params = activation_layer.grads_wrt_params(
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test_inputs, test_grads_wrt_outputs)
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test_output = "BatchNormalization:\nFprop: {}\nBprop: {}\nGrads_wrt_params: {}\n"\
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.format(BN_fprop, BN_bprop, BN_grads_wrt_params)
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with open("{}_batchnorm_test_file.txt".format(student_id), "w+") as out_file:
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out_file.write(test_output) |