https://www.lfd.uci.edu/~gohlke/pythonlibs/xmshzit7/ pip3 install numpy-1.11.3+mkl-cp35-cp35m-win_amd64.whl pip3 install scipy-0.19.0-cp35-cp35m-win_amd64.whl pip3 install h5py pip3 install --upgrade tensorflow pip3 install keras
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
from matplotlib import pyplot as plt
Using TensorFlow backend.
#Nacteni trenovacich a testovacich dat
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape)
(60000, 28, 28)
#Vykresleni prikladu trenovacich dat
plt.imshow(X_train[0])
plt.show()
#Potrebny reshape dat pred trenovanim (keras ocekava obrazek, tzv. trirozmerna dat)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
#Normalizace dat na hodnoty od 0 do 1
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
#Vytvoreni one-hot vektoru
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
#Vytvoreni sekvencniho modelu site
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_9 (Conv2D) (None, 26, 26, 32) 320 _________________________________________________________________ conv2d_10 (Conv2D) (None, 24, 24, 32) 9248 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32) 0 _________________________________________________________________ dropout_3 (Dropout) (None, 12, 12, 32) 0 _________________________________________________________________ flatten_2 (Flatten) (None, 4608) 0 _________________________________________________________________ dense_3 (Dense) (None, 128) 589952 _________________________________________________________________ dropout_4 (Dropout) (None, 128) 0 _________________________________________________________________ dense_4 (Dense) (None, 10) 1290 ================================================================= Total params: 600,810.0 Trainable params: 600,810.0 Non-trainable params: 0.0 _________________________________________________________________
C:\WinPython-64bit-3.5.3.1Qt5\python-3.5.3.amd64\lib\site-packages\ipykernel\__main__.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu", input_shape=(28, 28, 1...)` app.launch_new_instance() C:\WinPython-64bit-3.5.3.1Qt5\python-3.5.3.amd64\lib\site-packages\ipykernel\__main__.py:4: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu")`
#Kompilace modelu
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#Trenovani modelu
model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)
C:\WinPython-64bit-3.5.3.1Qt5\python-3.5.3.amd64\lib\site-packages\keras\models.py:826: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`. warnings.warn('The `nb_epoch` argument in `fit` '
Epoch 1/10 60000/60000 [==============================] - 19s - loss: 0.2075 - acc: 0.9362 Epoch 2/10 60000/60000 [==============================] - 16s - loss: 0.0887 - acc: 0.9728 Epoch 3/10 60000/60000 [==============================] - 15s - loss: 0.0654 - acc: 0.9801 Epoch 4/10 60000/60000 [==============================] - 15s - loss: 0.0552 - acc: 0.9829 Epoch 5/10 60000/60000 [==============================] - 16s - loss: 0.0501 - acc: 0.9845 Epoch 6/10 60000/60000 [==============================] - 17s - loss: 0.0422 - acc: 0.9874 Epoch 7/10 60000/60000 [==============================] - 17s - loss: 0.0384 - acc: 0.9877 Epoch 8/10 60000/60000 [==============================] - 16s - loss: 0.0332 - acc: 0.9899 Epoch 9/10 60000/60000 [==============================] - 16s - loss: 0.0303 - acc: 0.9903 Epoch 10/10 60000/60000 [==============================] - 17s - loss: 0.0303 - acc: 0.9903
<keras.callbacks.History at 0x2b95801b4a8>
#Evaluace modelu
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Test loss: 0.0334297043647 Test accuracy: 0.9901
#Jednotlive predikce modelu
predictions = model.predict(X_test)
print('First prediction:', np.argmax(predictions[0]))
print('Truth:', np.argmax(Y_test[0]))
First prediction: 7 Truth: 7