51 lines
1.5 KiB
Python
Executable File
51 lines
1.5 KiB
Python
Executable File
import numpy
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras.utils import np_utils
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numpy.random.seed(42)
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img_rows, img_cols = 28, 28
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
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X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
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input_shape = (img_rows, img_cols, 1)
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X_train = X_train.astype('float32')
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X_test = X_test.astype('float32')
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X_train /= 255
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X_test /= 255
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Y_train = np_utils.to_categorical(y_train, 10)
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Y_test = np_utils.to_categorical(y_test, 10)
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model = Sequential()
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model.add(Conv2D(75, kernel_size=(5, 5),
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activation='relu',
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input_shape=input_shape))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.2))
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model.add(Conv2D(100, (5, 5), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.2))
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model.add(Flatten())
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model.add(Dense(500, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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model.summary()
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model.fit(X_train, Y_train, batch_size=200, epochs=10, validation_split=0.2, verbose=1)
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scores = model.evaluate(X_test, Y_test, verbose=0)
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print("Dokadnosc na testowanych dannych: %.2f%%" % (scores[1]*100))
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model.save_weights('model_weights.h5') |