sieci neuronowe

sieci_n.py

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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')