54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
from sklearn import datasets
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import numpy as np
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from layer import *
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import os
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import pickle
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import matplotlib
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import matplotlib.pyplot as plt
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import random
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import itertools
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import math
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import mnist_load
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from p4_model import *
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#matplotlib.use("TkAgg")
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train_set, dev_set, test_set = mnist_load.load_mnistdata()
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train_x,train_y = train_set
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dev_x,dev_y = dev_set
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test_x,test_y = test_set
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gen:np.random.Generator = np.random.default_rng()
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eta = 0.0001
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MiniBatchN = 32
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model = load_or_create_model([300,10])
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end_n = math.floor(3500*17 /MiniBatchN)
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J = model.caculate(dev_x,dev_y)
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loss = np.average(J.numpy())
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print(make_mermaid_graph(J))
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print('testset : avg loss : ',loss)
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confusion = get_confusion(J)
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accuracy = get_accuracy_from_confusion(confusion)
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print('accuracy : {:.2f}%'.format(accuracy * 100))
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plt.subplot(1,2,1)
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plt.title("accuracy")
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plt.plot([*map(lambda x: x.iteration,model.checkpoints)],
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[*map(lambda x: x.accuracy,model.checkpoints)]
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)
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plt.subplot(1,2,2)
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plt.title("loss")
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plt.plot([*map(lambda x: x.iteration,model.checkpoints)],
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[*map(lambda x: x.loss,model.checkpoints)])
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plt.show()
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plt.title("confusion matrix")
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plt.imshow(confusion,cmap='Blues')
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plt.colorbar()
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for i,j in itertools.product(range(confusion.shape[0]),range(confusion.shape[1])):
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plt.text(j,i,"{:}".format(confusion[i,j]),horizontalalignment="center",color="white" if i == j else "black")
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plt.show() |