def test():
network = SimpleConvNet(input_dim=(3,128,128),
conv_param = {'filter_num': [32,32], 'filter_size': [3,3], 'pad': [1,1], 'stride': [1,1]},
hidden_size=100, output_size=12, weight_init_std=0.01)
network.load_params("params.pkl")
data_transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
path = join('dataset\plant-seedlings-classification', 'test')
sample_submission = pd.read_csv('sample_submission.csv')
submission = sample_submission.copy()
for i, f in enumerate(sample_submission['file']):
print(join(path, f))
image = Image.open(join(path, f))
#convert image
if image.mode != "RGB":
image = image.convert("RGB")
image = data_transform(image)
image = np.expand_dims(image, axis=0)
output = network.predict(image)
output = np.argmax(output, axis=1)
submission['species'][i] = one_hot_key[output[0]]
submission.to_csv('submission.csv', index=False)
ny = int(np.ceil(show_num / nx))
fig = plt.figure()
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(show_num):
ax = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[])
ax.imshow(filters[i, 0], cmap=plt.cm.gray_r, interpolation='nearest')
network = SimpleConvNet(input_dim=(1,28,28),
conv_param = {'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1},
hidden_size=100, output_size=10, weight_init_std=0.01)
# 学習後の重み
network.load_params("params.pkl")
filter_show(network.params['W1'], 16)
img = imread('../dataset/lena_gray.png')
img = img.reshape(1, 1, *img.shape)
fig = plt.figure()
w_idx = 1
for i in range(16):
w = network.params['W1'][i]
b = 0 # network.params['b1'][i]
w = w.reshape(1, *w.shape)
ax.imshow(filters[i, 0], cmap=plt.cm.gray_r, interpolation='nearest')
network = SimpleConvNet(input_dim=(1, 28, 28),
conv_param={
'filter_num': 30,
'filter_size': 5,
'pad': 0,
'stride': 1
},
hidden_size=100,
output_size=10,
weight_init_std=0.01)
# 学习后的权重
network.load_params("params.pkl")
filter_show(network.params['W1'], 16)
img = imread('../dataset/lena_gray.png')
img = img.reshape(1, 1, *img.shape)
fig = plt.figure()
w_idx = 1
for i in range(16):
w = network.params['W1'][i]
b = 0 # network.params['b1'][i]
w = w.reshape(1, *w.shape)
@author: ghqls
"""
import numpy as np
import matplotlib.pyplot as plt
from simple_convnet import SimpleConvNet
def filter_show(filters, nx=8, margin=3, scale=10):
FN, C, FH, FW = filters.shape
ny = int(np.ceil(FN / nx))
fig = plt.figure()
fig.subplots_adjust(left=0,
right=1,
bottom=0,
top=1,
hspace=0.05,
wspace=0.05)
for i in range(FN):
ax = fig.add_subplot(ny, nx, i + 1, xticks=[], yticks=[])
ax.imshow(filters[i, 0], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show()
network = SimpleConvNet()
filter_show(network.params['W1'])
network.load_params('params.pkl')
filter_show(network.params['W1'])
(_, _), (x_test, _) = load_mnist(normalize=True, flatten=False, one_hot_label=False)
# 初始化网络
# 网络1：简单CNN
"""
conv - relu - pool - affine - relu - affine - softmax
"""
# 建立一个卷积神经网络的类。
# 本人对于卷积神经网络还不是很了解，不过其实现思路和我前面写的my_app_cnn中的卷积神经网络的步骤相同。
# 关于卷积神经网络将在后面学习，但是这次没有使用tf
network = SimpleConvNet(input_dim=(1,28,28),
conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},
hidden_size=100, output_size=10, weight_init_std=0.01)
network.load_params("mnist-master\mnist-master\params.pkl")
# 网络2：深度CNN
# network = DeepConvNet()
# network.load_params("deep_convnet_params.pkl")
class MainWindow(QMainWindow,Ui_MainWindow):
def __init__(self):
# 初始化继承的父类(Qmainwindow)
super(MainWindow,self).__init__()
# 初始化参数
self.mode = MODE_MNIST
self.result = [0, 0]
ax.imshow(filters[i, 0], cmap=plt.cm.gray_r, interpolation='nearest')
network = SimpleConvNet(input_dim=(1, 28, 28),
conv_param={
'filter_num': 30,
'filter_size': 5,
'pad': 0,
'stride': 1
},
hidden_size=100,
output_size=10,
weight_init_std=0.01)
# 学習後の重み
network.load_params("./ch07/params.pkl")
filter_show(network.params['W1'], 16)
img = imread('./dataset/lena_gray.png')
img = img.reshape(1, 1, *img.shape)
fig = plt.figure()
w_idx = 1
for i in range(16):
w = network.params['W1'][i]
b = 0 # network.params['b1'][i]
w = w.reshape(1, *w.shape)
def filter_show(filters, nx=8, margin=3, scale=10):
"""
c.f. https://gist.github.com/aidiary/07d530d5e08011832b12#file-draw_weight-py
"""
FN, C, FH, FW = filters.shape
ny = int(np.ceil(FN / nx))
fig = plt.figure()
fig.subplots_adjust(left=0,
right=1,
bottom=0,
top=1,
hspace=0.05,
wspace=0.05)
for i in range(FN):
ax = fig.add_subplot(ny, nx, i + 1, xticks=[], yticks=[])
ax.imshow(filters[i, 0], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show()
network = SimpleConvNet()
# ランダム初期化後の重み
##filter_show(network.params['W1'])
# 学習後の重み
network.load_params("deep_convnet_params9.pkl")
filter_show(network.params['W1'])
