pickle保存mnist手写数字识别模型的代码
时间: 2023-04-06 21:03:11 浏览: 85
以下是保存mnist手写数字识别模型的代码:
```
import pickle
import gzip
import numpy as np
import matplotlib.pyplot as plt
import urllib.request
# 加载数据集
def load_data():
urllib.request.urlretrieve('http://deeplearning.net/data/mnist/mnist.pkl.gz', 'mnist.pkl.gz')
with gzip.open('mnist.pkl.gz', 'rb') as f:
training_data, validation_data, test_data = pickle.load(f, encoding='latin1')
return (training_data, validation_data, test_data)
# 加载数据集
training_data, validation_data, test_data = load_data()
# 保存模型
with open('mnist_model.pkl', 'wb') as f:
pickle.dump(model, f)
```
希望这个代码对你有帮助!
相关问题
决策树手写数字识别代码
以下是一个简单的决策树手写数字识别代码的示例:
```python
import numpy as np
class DecisionTree:
def __init__(self, depth=1):
self.depth = depth
self.tree = {}
def fit(self, X, y):
self.tree = self.build_tree(X, y, self.depth)
def predict(self, X):
y_pred = [self.predict_one(x, self.tree) for x in X]
return np.array(y_pred)
def predict_one(self, x, tree):
if isinstance(tree, int):
return tree
else:
feature, value, left_subtree, right_subtree = tree
if x[feature] <= value:
return self.predict_one(x, left_subtree)
else:
return self.predict_one(x, right_subtree)
def build_tree(self, X, y, depth):
if len(set(y)) == 1:
return y[0]
elif depth == 0:
return self.get_majority(y)
else:
feature, value = self.get_best_split(X, y)
left_X, left_y, right_X, right_y = self.split_data(X, y, feature, value)
left_subtree = self.build_tree(left_X, left_y, depth-1)
right_subtree = self.build_tree(right_X, right_y, depth-1)
return feature, value, left_subtree, right_subtree
def get_best_split(self, X, y):
best_feature, best_value, best_score = None, None, -1
for feature in range(X.shape[1]):
for value in np.unique(X[:, feature]):
left_X, left_y, right_X, right_y = self.split_data(X, y, feature, value)
score = self.gini_index(left_y) * len(left_y) + self.gini_index(right_y) * len(right_y)
if score > best_score:
best_feature, best_value, best_score = feature, value, score
return best_feature, best_value
def split_data(self, X, y, feature, value):
left_idx = X[:, feature] <= value
right_idx = X[:, feature] > value
left_X, left_y = X[left_idx], y[left_idx]
right_X, right_y = X[right_idx], y[right_idx]
return left_X, left_y, right_X, right_y
def gini_index(self, y):
_, counts = np.unique(y, return_counts=True)
p = counts / len(y)
return 1 - np.sum(p**2)
def get_majority(self, y):
_, counts = np.unique(y, return_counts=True)
return np.argmax(counts)
```
在这个示例中,我们使用基尼不纯度(gini impurity)作为分裂标准,构建了一棵深度为1的决策树。我们可以将该决策树应用于手写数字识别,使用MNIST数据集进行训练和测试。具体的实现细节可以参考以下示例代码:
```python
import gzip
import pickle
def load_data():
with gzip.open('./data/mnist.pkl.gz', 'rb') as f:
train_data, _, test_data = pickle.load(f, encoding='latin1')
X_train, y_train = train_data
X_test, y_test = test_data
return X_train, y_train, X_test, y_test
X_train, y_train, X_test, y_test = load_data()
tree = DecisionTree(depth=1)
tree.fit(X_train, y_train)
y_pred = tree.predict(X_test)
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)
```
在这个示例中,我们将MNIST数据集加载到内存中,使用决策树对数据进行训练和测试,并计算模型的准确度。
puyorch实现手写数字识别GUI界面代码
以下是一个简单的PyTorch手写数字识别的GUI界面代码示例,供参考:
```python
import torch
import numpy as np
from PIL import Image, ImageDraw
import tkinter as tk
import pickle
# 加载训练好的PyTorch模型
model = torch.load('model.pkl')
# 创建GUI界面
root = tk.Tk()
root.title('Handwritten Digit Recognition')
# 创建画布
canvas_width = 200
canvas_height = 200
canvas = tk.Canvas(root, width=canvas_width, height=canvas_height, bg='white')
canvas.pack()
# 创建文本框
text = tk.StringVar()
result_label = tk.Label(root, textvariable=text, font=('Arial', 20))
result_label.pack()
# 创建清除按钮
def clear_canvas():
canvas.delete('all')
text.set('')
clear_button = tk.Button(root, text='Clear', command=clear_canvas)
clear_button.pack()
# 创建预测按钮
def predict_digit():
# 将画布上的图像转换为PyTorch张量
img = canvas_to_tensor()
# 使用模型进行预测
with torch.no_grad():
output = model(img)
prediction = torch.argmax(output, dim=1)
text.set('Prediction: ' + str(prediction.item()))
predict_button = tk.Button(root, text='Predict', command=predict_digit)
predict_button.pack()
# 将画布上的图像转换为PyTorch张量
def canvas_to_tensor():
img = canvas.postscript(colormode='color')
pil_image = Image.open(io.BytesIO(img.encode('utf-8')))
pil_image = pil_image.resize((28, 28), Image.ANTIALIAS).convert('L')
pil_image = ImageOps.invert(pil_image)
np_image = np.array(pil_image)
tensor = torch.from_numpy(np_image).float().div(255)
tensor = tensor.view(1, 1, 28, 28)
return tensor
root.mainloop()
```
这个程序创建了一个GUI界面,包括一个200x200的画布、一个文本框和两个按钮。用户可以在画布上绘制手写数字,然后单击“Predict”按钮进行预测,预测结果将显示在文本框中。如果用户想要清除画布,可以单击“Clear”按钮。请确保您已经训练好了PyTorch手写数字识别模型,并将其保存为.pkl文件。在代码中,您需要将“model.pkl”替换为您自己的模型文件名。