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run.py
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run.py
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# def SGD(self, training_data, epochs, mini_batch_size, eta, validation_data, test_data, training_outputs, weight_file = None, variable_updates = False, log_file=None, lmbda=0.0):
# from theano.tensor.nnet import sigmoid
# from theano.tensor import tanh
import network3, pickle
from network3 import Network, ReLU
from network3 import ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer
p = [ 2, 5, 9, 12, 15, 18, 21, 24, 260, 263, 266, 269,
272, 275, 278, 457, 460, 463, 466, 470, 473, 476, 479, 482,
485, 488, 491, 494, 497, 500, 503, 506, 509, 512, 0, 1,
7, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
85, 86, 87, 88, 89, 90, 91, 3, 6, 10, 13, 16,
19, 22, 25, 261, 264, 267, 270, 273, 276, 279, 458, 461,
464, 468, 471, 474, 477, 480, 483, 486, 489, 492, 495, 498,
501, 504, 507, 510, 513, 92, 93, 94, 95, 96, 97, 98,
99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,
123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,
159, 160, 4, 8, 11, 14, 17, 20, 23, 259, 262, 265,
268, 271, 274, 277, 441, 459, 462, 465, 469, 472, 475, 478,
481, 484, 487, 490, 493, 496, 499, 502, 505, 508, 511, 514,
161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,
233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,
245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256,
257, 258, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301,
302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,
326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349,
350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361,
362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373,
374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397,
398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409,
410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421,
422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,
434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 445, 446,
447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 467]
#weight file
# file = open('weights/2259.pckl', 'rb')
# metadata = pickle.load(file)
# file.close()
#CNN
dataSet = 1458
training_data, validation_data, test_data, training_outputs = network3.load_data_shared(filename = str(dataSet)+'PartialHalf', perm = p)
mini_batch_size = 300
net = Network([
ConvPoolLayer(image_shape=(mini_batch_size, 1, 103, 5),
filter_shape=(7, 1, 3, 3),
poolsize=(1, 1)),
ConvPoolLayer(image_shape=(mini_batch_size, 7, 101, 3),
filter_shape=(7, 7, 3, 3),
poolsize=(1, 1)),
FullyConnectedLayer(n_in=7 * 99 * 1, n_out=600),
FullyConnectedLayer(n_in=600, n_out=500),
SoftmaxLayer(n_in=500, n_out=2)], mini_batch_size)
net.SGD(training_data, 1200000000, mini_batch_size, .5,
validation_data, test_data,training_outputs, weight_file = str(dataSet)+'CNN', variable_updates = False, log_file = str(dataSet)+'CNN',lmbda = .0001)
#MLP
# dataSet = 1458
training_data, validation_data, test_data, training_outputs = network3.load_data_shared(filename = str(dataSet)+'PartialHalf', perm = p)
mini_batch_size = 300
net = Network([
FullyConnectedLayer(n_in=515, n_out=400),
FullyConnectedLayer(n_in=400, n_out=400),
SoftmaxLayer(n_in=400, n_out=2)], mini_batch_size)
net.SGD(training_data, 10000000, mini_batch_size, .1,
validation_data, test_data, training_outputs, weight_file = str(dataSet)+'MLP', variable_updates = False, log_file = str(dataSet)+'MLP', lmbda = .0001)