simple_convnet4_original.py
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import sys, os
sys.path.append(os.pardir)
import pickle
import numpy as cp
import numpy as np
from collections import OrderedDict
from layers import *
from gradient import numerical_gradient
class SimpleConvNet:
def __init__(self, input_dim=(3, 32, 32),
conv_param={'filter_num':(32, 32, 64), 'filter_size':3, 'pad':1, 'stride':1},
hidden_size=512, output_size=10, weight_init_std=0.01):
filter_num = conv_param['filter_num']
filter_size = conv_param['filter_size']
filter_pad = conv_param['pad']
filter_stride = conv_param['stride']
input_size = input_dim[1]
conv_output_size = (input_size - filter_size + 2*filter_pad) / filter_stride + 1
conv_data_size = int(filter_num[0] * conv_output_size * conv_output_size )
pool1_output_size = int(filter_num[1] * (conv_output_size/2) * (conv_output_size/2))
pool2_output_size = int(filter_num[2] * (conv_output_size/4) * (conv_output_size/4))
pool3_output_size = int(filter_num[2] * (conv_output_size/8) * (conv_output_size/8))
self.params = {}
self.params['W1'] = cp.array( weight_init_std * \
cp.random.randn(filter_num[0], input_dim[0], filter_size, filter_size), dtype=np.float32)
self.params['W2'] = cp.array( weight_init_std * \
cp.random.randn(filter_num[1], filter_num[0], 1, 1), dtype=np.float32)
self.params['W3'] = cp.array( weight_init_std * \
cp.random.randn(filter_num[1], 1, filter_size, filter_size), dtype=np.float32)
self.params['W4'] = cp.array( weight_init_std * \
cp.random.randn(filter_num[2], filter_num[1], 1, 1), dtype=np.float32)
self.params['W5'] = cp.array( weight_init_std * \
cp.random.randn(filter_num[2], 1, filter_size, filter_size), dtype=np.float32)
self.params['W6'] = cp.array( weight_init_std * \
cp.random.randn(pool3_output_size, hidden_size), dtype=np.float32)
self.params['W7'] = cp.array( weight_init_std * \
cp.random.randn(hidden_size, output_size), dtype=np.float32)
self.layers = OrderedDict()
self.layers['Conv1'] = Convolution(self.params['W1'],
conv_param['stride'], conv_param['pad'])
self.layers['LightNorm1'] = LightNormalization()
self.layers['Relu1'] = Relu()
self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2)
self.layers['Conv2'] = Convolution(self.params['W2'],
1, 0)
self.layers['LightNorm2'] = LightNormalization()
self.layers['Relu2'] = Relu()
self.layers['Conv3'] = DW_Convolution(self.params['W3'],
conv_param['stride'], conv_param['pad'])
self.layers['LightNorm3'] = LightNormalization()
self.layers['Relu3'] = Relu()
self.layers['Pool2'] = Pooling(pool_h=2, pool_w=2, stride=2)
self.layers['Conv4'] = Convolution(self.params['W4'],
1, 0)
self.layers['LightNorm4'] = LightNormalization()
self.layers['Relu4'] = Relu()
self.layers['Conv5'] = DW_Convolution(self.params['W5'],
conv_param['stride'], conv_param['pad'])
self.layers['LightNorm5'] = LightNormalization()
self.layers['Relu5'] = Relu()
self.layers['Pool3'] = Pooling(pool_h=2, pool_w=2, stride=2)
self.layers['Affine4'] = Affine(self.params['W6'])
self.layers['LightNorm6'] = LightNormalization()
self.layers['Relu6'] = Relu()
self.layers['Affine5'] = Affine(self.params['W7'])
self.last_layer = SoftmaxWithLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
def loss(self, x, t):
y = self.predict(x)
return self.last_layer.forward(y, t)
def accuracy(self, x, t, batch_size=100):
if t.ndim != 1 : t = np.argmax(t, axis=1)
acc = 0.0
for i in range(int(x.shape[0] / batch_size)):
tx = x[i*batch_size:(i+1)*batch_size]
tt = t[i*batch_size:(i+1)*batch_size]
y = self.predict(tx)
y = np.argmax(y, axis=1)
acc += np.sum(y == tt) #numpy
return acc / x.shape[0]
def gradient(self, x, t):
self.loss(x, t)
dout = 1
dout = self.last_layer.backward(dout)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)
grads = {}
grads['W1'] = self.layers['Conv1'].dW
grads['W2'] = self.layers['Conv2'].dW
grads['W3'] = self.layers['Conv3'].dW
grads['W4'] = self.layers['Conv4'].dW
grads['W5'] = self.layers['Conv5'].dW
grads['W6'] = self.layers['Affine4'].dW
grads['W7'] = self.layers['Affine5'].dW
return grads
def save_params(self, file_name="params.pkl"):
params = {}
for key, val in self.params.items():
params[key] = val
with open(file_name, 'wb') as f:
pickle.dump(params, f)