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Finished draft of part1

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Grant Sanderson
2017-10-02 12:59:23 -07:00
parent 7f69b6aa93
commit c474207c34
5 changed files with 4925 additions and 48 deletions
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7
nn/network.py
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@ -21,13 +21,14 @@ import cPickle
from nn.mnist_loader import load_data_wrapper
NN_DIRECTORY = os.path.dirname(os.path.realpath(__file__))
# PRETRAINED_DATA_FILE = os.path.join(NN_DIRECTORY, "pretrained_weights_and_biases_36")
PRETRAINED_DATA_FILE = os.path.join(NN_DIRECTORY, "pretrained_weights_and_biases")
IMAGE_MAP_DATA_FILE = os.path.join(NN_DIRECTORY, "image_map")
# PRETRAINED_DATA_FILE = "/Users/grant/cs/manim/nn/pretrained_weights_and_biases_on_zero"
# DEFAULT_LAYER_SIZES = [28**2, 36, 10]
DEFAULT_LAYER_SIZES = [28**2, 16, 16, 10]
class Network(object):
def __init__(self, sizes):
"""The list ``sizes`` contains the number of neurons in the
respective layers of the network. For example, if the list
@ -192,12 +193,12 @@ def test_network():
network = get_pretrained_network()
training_data, validation_data, test_data = load_data_wrapper()
n_right, n_wrong = 0, 0
for test_in, test_out in test_data[:30]:
for test_in, test_out in test_data:
if np.argmax(network.feedforward(test_in)) == test_out:
n_right += 1
else:
n_wrong += 1
print float(n_right)/(n_right + n_wrong)
print n_right, n_wrong, float(n_right)/(n_right + n_wrong)
def layer_to_image_array(layer):
w = int(np.ceil(np.sqrt(len(layer))))