sieci neuronowe

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Daria Vodzinska 2020-06-08 12:05:11 +00:00
parent 4b1d75f28f
commit 385afbf00a

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sieci_n.py Normal file
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import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.preprocessing import image
from matplotlib import pyplot as plt
import matplotlib.pyplot as plt
# %matplotlib inline
from google.colab import files
numpy.random.seed(42)
img_rows, img_cols = 28, 28
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(75, kernel_size=(5, 5),
activation='relu',
input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(100, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.summary()
model.fit(X_train, Y_train, batch_size=200, epochs=10, validation_split=0.2, verbose=1)
scores = model.evaluate(X_test, Y_test, verbose=0)
print("Dokadnosc na testowanych dannych: %.2f%%" % (scores[1]*100))
model.save_weights('model_weights.h5')