new dataset
This commit is contained in:
shaaqu
parent
d07d2ce137
commit
239eaf7d97
@ -19,7 +19,9 @@
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@ -169,6 +172,9 @@
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<module name="wozek" />
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@ -188,6 +194,9 @@
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<module name="wozek" />
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@ -204,16 +213,16 @@
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@ -307,7 +321,14 @@
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@ -336,7 +357,8 @@
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<MESSAGE value="recognizing but training must be improved" />
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28001
coder/dataset/test.csv
Normal file
28001
coder/dataset/test.csv
Normal file
File diff suppressed because one or more lines are too long
42001
coder/dataset/train.csv
Normal file
42001
coder/dataset/train.csv
Normal file
File diff suppressed because one or more lines are too long
@ -4,37 +4,55 @@ import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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import pandas as pd
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import cv2
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#28x28
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train_data = np.genfromtxt('dataset/train.csv', delimiter=',', skip_header=1 ,max_rows=20000, encoding='utf-8')
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test_data = np.genfromtxt('dataset/test.csv', delimiter=',' , skip_header=1, max_rows=20000, encoding='utf-8')
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# training
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# recznie napisane cyfry
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digits = datasets.load_digits()
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digits = datasets.load_digits()
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y = digits.target
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x = digits.images.reshape((len(digits.images), -1))
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#ogarnac zbior, zwiekszyc warstwy
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x_train = x[:1000000]
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y_train = y[:1000000]
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x_test = x[1000000:]
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y_test = y[1000000:]
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x_train = train_data[0:20000, 1:]
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y_train = train_data[0:20000, 0]
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x_test = test_data[0:20000]
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y_test = test_data[0:20000, 0]
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mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
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solver='sgd', tol=1e-4, random_state=1,
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learning_rate_init=.1, verbose=True)
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# x_train = x[:900]
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# y_train = y[:900]
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# x_test = x[900:]
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# y_test = y[900:]
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print(x_test[0].shape, y_test[9].shape)
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mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100, 100), activation='logistic', alpha=1e-4,
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solver='sgd', tol=0.000000000001, random_state=1,
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learning_rate_init=.1, verbose=True, max_iter=1000)
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mlp.fit(x_train, y_train)
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print(123456789)
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predictions = mlp.predict(x_test)
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print(accuracy_score(y_test, predictions))
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print(123456789)
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print("Accuracy: ", accuracy_score(y_test, predictions))
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# image
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img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
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img = cv2.cvtColor(cv2.imread('test5.jpg'), cv2.COLOR_BGR2GRAY)
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img = cv2.blur(img, (9, 9)) # poprawia jakosc
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img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
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img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
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img = img.reshape((len(img), -1))
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print(type(img))
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print(img.shape)
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@ -61,4 +79,3 @@ print(data)
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predictions = mlp.predict([data])
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print("Liczba to:", predictions[0])
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