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Merge branch 'mlp2017-8/coursework_2' of https://github.com/CSTR-Edinburgh/mlpractical into mlp2017-8/coursework_2

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AntreasAntoniou 2017-11-07 12:55:41 +00:00
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scripts/generate_inputs.py
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import numpy as np
from mlp.layers import ConvolutionalLayer, BatchNormalizationLayer
import argparse
parser = argparse.ArgumentParser(description='Welcome to GAN-Shot-Learning script')
parser.add_argument('--student_id', nargs="?", type=str, help='Your student id in the format "sxxxxxxx"')
args = parser.parse_args()
student_id = args.student_id
def generate_inputs(student_id):
student_number = student_id
tests = np.arange(96).reshape((2, 3, 4, 4))
tests[:, 0, :, :] = float(student_number[1:3]) / 10 - 5
tests[:, :, 1, :] = float(student_number[3:5]) / 10 - 5
tests[:, 2, :, :] = float(student_number[5:7]) / 10 - 5
tests[0, 1, :, :] = float(student_number[7]) / 10 - 5
return tests
test_inputs = generate_inputs(student_id)
test_grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))
inputs = np.arange(96).reshape((2, 3, 4, 4))
kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))
biases = np.arange(2)
#produce ConvolutionalLayer fprop, bprop and grads_wrt_params
activation_layer = ConvolutionalLayer(num_input_channels=3, num_output_channels=2, input_dim_1=4, input_dim_2=4,
kernel_dim_1=2, kernel_dim_2=2)
activation_layer.params = [kernels, biases]
conv_fprop = activation_layer.fprop(test_inputs)
conv_bprop = activation_layer.bprop(
test_inputs, conv_fprop, test_grads_wrt_outputs)
conv_grads_wrt_params = activation_layer.grads_wrt_params(test_inputs, test_grads_wrt_outputs)
test_inputs = np.reshape(test_inputs, newshape=(2, -1))
test_grads_wrt_outputs = np.arange(-48, 48).reshape((2, -1))
#produce ELU fprop and bprop
activation_layer = BatchNormalizationLayer(input_dim=48)
beta = np.array(48*[0.3])
gamma = np.array(48*[0.8])
activation_layer.params = [gamma, beta]
BN_fprop = activation_layer.fprop(test_inputs)
BN_bprop = activation_layer.bprop(
test_inputs, BN_fprop, test_grads_wrt_outputs)
BN_grads_wrt_params = activation_layer.grads_wrt_params(
test_inputs, test_grads_wrt_outputs)
test_output = "ConvolutionalLayer:\nFprop: {}\nBprop: {}\nGrads_wrt_params: {}\n" \
"BatchNormalization:\nFprop: {}\nBprop: {}\nGrads_wrt_params: {}\n"\
.format(conv_fprop,
conv_bprop,
conv_grads_wrt_params,
BN_fprop, BN_bprop, BN_grads_wrt_params)
with open("{}_test_file.txt".format(student_id), "w+") as out_file:
out_file.write(test_output)