anton/mlpractical
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added generation routine for cross correlation

Browse Source
This commit is contained in:
AntreasAntoniou 2017-11-25 16:43:33 +00:00
parent 1d2aabc2b3
commit 65508a7712
1 changed files with 16 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
scripts/generate_conv_test.py
View File

@ -19,8 +19,6 @@ def generate_inputs(student_id):
tests[0, 1, :, :] = float(student_number[7]) / 10 - 5 tests[0, 1, :, :] = float(student_number[7]) / 10 - 5
return tests return tests
test_inputs = generate_inputs(student_id) test_inputs = generate_inputs(student_id)
test_grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3)) test_grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))
inputs = np.arange(96).reshape((2, 3, 4, 4)) inputs = np.arange(96).reshape((2, 3, 4, 4))
@ -36,11 +34,26 @@ conv_bprop = activation_layer.bprop(
test_inputs, conv_fprop, test_grads_wrt_outputs) test_inputs, conv_fprop, test_grads_wrt_outputs)
conv_grads_wrt_params = activation_layer.grads_wrt_params(test_inputs, conv_grads_wrt_params = activation_layer.grads_wrt_params(test_inputs,
test_grads_wrt_outputs) test_grads_wrt_outputs)
test_output = "ConvolutionalLayer:\nFprop: {}\nBprop: {}\n" \ test_output = "ConvolutionalLayer:\nFprop: {}\nBprop: {}\n" \
"Grads_wrt_params: {}\n".format(conv_fprop, "Grads_wrt_params: {}\n".format(conv_fprop,
conv_bprop, conv_bprop,
conv_grads_wrt_params) conv_grads_wrt_params)
cross_correlation_kernels = kernels[:, :, ::-1, ::-1]
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 = [cross_correlation_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_cross_correlation_output = "Cross_Correlation_ConvolutionalLayer:\nFprop: {}\nBprop: {}\n" \
"Grads_wrt_params: {}\n".format(conv_fprop,
conv_bprop,
conv_grads_wrt_params)
test_output = test_output + "\n" + test_cross_correlation_output
with open("{}_conv_test_file.txt".format(student_id), "w+") as out_file: with open("{}_conv_test_file.txt".format(student_id), "w+") as out_file:
out_file.write(test_output) out_file.write(test_output)