cwpearson/ece408-backprop-demo
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hyperparams

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Carl Pearson
2016-11-16 15:53:45 -06:00
parent 23bf172b96
commit 8850a53f5a
1 changed files with 13 additions and 16 deletions
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29
network.py
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@@ -13,7 +13,7 @@ def dataset_get_sin():
RATIO = 0.5 RATIO = 0.5
SPLIT = int(NUM * RATIO) SPLIT = int(NUM * RATIO)
data = np.zeros((NUM, 2), DATA_TYPE) data = np.zeros((NUM, 2), DATA_TYPE)
data[:, 0] = np.linspace(0.0, 2 * np.pi, num=NUM) # inputs data[:, 0] = np.linspace(0.0, 4 * np.pi, num=NUM) # inputs
data[:, 1] = np.sin(data[:, 0]) # outputs data[:, 1] = np.sin(data[:, 0]) # outputs
npr.shuffle(data) npr.shuffle(data)
training, test = data[:SPLIT, :], data[SPLIT:, :] training, test = data[:SPLIT, :], data[SPLIT:, :]
@@ -61,33 +61,30 @@ def L(x, y):
class Model(object): class Model(object):
def __init__(self, layer_size, h, dh, data_type): def __init__(self, layer_size, h, dh, data_type):
self.w1 = npr.uniform(-1, 1, layer_size) self.w1 = npr.uniform(0, 1, layer_size)
self.b1 = npr.uniform(-1, 1, layer_size) self.b1 = npr.uniform(0, 1, layer_size)
self.w2 = npr.uniform(-1, 1, (1, layer_size)) self.w2 = npr.uniform(0, 1, (1, layer_size))
self.b2 = npr.uniform(-1, 1, 1) self.b2 = npr.uniform(0, 1, 1)
self.w1 = preprocessing.scale(self.w1) # self.w1 = preprocessing.scale(self.w1)
self.w2 = preprocessing.scale(self.w2) # self.w2 = preprocessing.scale(self.w2)
self.b1 = preprocessing.scale(self.b1) # self.b1 = preprocessing.scale(self.b1)
self.b2 = preprocessing.scale(self.b2) # self.b2 = preprocessing.scale(self.b2)
self.h = h self.h = h
self.dh = dh self.dh = dh
def z1(self, x): def z1(self, x):
return self.w1 * x + self.b1 return self.w1 * x + self.b1
def a(self, x): def a(self, x):
return self.h(self.z1(x)) return self.h(self.z1(x))
def f(self, x): def f(self, x):
return self.w2.dot(self.a(x)) + self.b2 return self.w2.dot(self.a(x)) + self.b2
def dLdf(self, x, y): def dLdf(self, x, y):
return 2.0 * (self.f(x) - y) return -2.0 * (y - self.f(x))
def dfdb2(self): def dfdb2(self):
return np.array([1.0]) return np.array([1.0])
@@ -181,8 +178,8 @@ MODEL = Model(10, sigmoid, d_sigmoid, DATA_TYPE)
# MODEL = Model(10, relu, d_relu, DATA_TYPE) # MODEL = Model(10, relu, d_relu, DATA_TYPE)
# Train the model with some training data # Train the model with some training data
TRAINING_ITERS = 1000 TRAINING_ITERS = 5000
LEARNING_RATE = 0.005 LEARNING_RATE = 0.002
TRAINING_SUBSET_SIZE = len(TRAIN_DATA) TRAINING_SUBSET_SIZE = len(TRAIN_DATA)
print TRAINING_SUBSET_SIZE print TRAINING_SUBSET_SIZE
@@ -200,7 +197,7 @@ for training_iter in range(TRAINING_ITERS):
# MODEL.backward(training_subset, LEARNING_RATE) # MODEL.backward(training_subset, LEARNING_RATE)
# Apply backprop with minibatch # Apply backprop with minibatch
BATCH_SIZE = 2 BATCH_SIZE = 4
for i in range(0, len(training_subset), BATCH_SIZE): for i in range(0, len(training_subset), BATCH_SIZE):
batch = training_subset[i:min(i+BATCH_SIZE, len(training_subset))] batch = training_subset[i:min(i+BATCH_SIZE, len(training_subset))]
# print batch # print batch