def main(): num_classes = 10 x_train, y_train, x_test, y_test, input_shape = prepareMnistData( 0.1) #input_shape 28*28*1 model = createModel(input_shape, num_classes) file = r'./weights/cnnTf2Kernel_2.h5' if 1: #training model.load_weights(file) #continue training checkpointer = ModelCheckpoint(filepath=file, verbose=0, save_best_only=False) history = model.fit(x=x_train, y=y_train, batch_size=800, epochs=30, callbacks=[checkpointer]) #printModelWeights(model) loss = np.array(history.history['loss']) acc = np.array(history.history['accuracy']) #print('loss=',loss) #print('acc=', acc) #plotSubLossAndAcc(loss,acc) else: #load from pretrained file model.load_weights(file) test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc, 'loss=', test_loss)
def main(): num_classes = 10 x_train, y_train, x_test, y_test, input_shape = prepareMnistData(0.1) print('x_train.shape = ',x_train.shape) print('input_shape.shape = ',input_shape) model = createModel(input_shape,num_classes) model.fit(x_train, y_train, epochs=5)
def main(): num_classes = 10 x_train, y_train, x_test, y_test, input_shape = prepareMnistData( 0.2) #input_shape 28*28*1 model = createModel(input_shape, num_classes) history = model.fit(x=x_train, y=y_train, epochs=50) #printModelWeights(model) test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc, 'loss=', test_loss) loss = np.array(history.history['loss']) acc = np.array(history.history['accuracy']) plotSubLossAndAcc(loss, acc)
def main(): x_train, y_train, x_test, y_test, input_shape = prepareMnistData( 0.1) #input_shape 28*28*1 model = createModel(input_shape, classes=10) file = r'./weights/cnnTf2Kernel_2.h5' #r'./weights/cnnTf2Kernel.h5' model.load_weights(file) test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc, 'loss=', test_loss) #visualKernels(model) #visualize_filter(model) show32FilterImg(model)
def main(): x_train, y_train, x_test, y_test, input_shape = prepareMnistData(0.1) #input_shape 28*28*1 model = createModel(input_shape, classes=10) file = r'./weights/cnnTf2Kernel_2.h5' #r'./weights/cnnTf2Kernel.h5' model.load_weights(file) test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc,'loss=',test_loss) testImg = x_test[6] testLabel = y_test[6] # print('testImg:', testImg.shape) # print('testLabel:', testLabel) # plt.imshow(testImg) # plt.show() visualModel(model, testImg)
def main(): x_train, y_train, x_test, y_test, input_shape = prepareMnistData(0.1) #input_shape 28*28*1 model = createModel(input_shape, classes=10) file = r'./weights/cnnTf2Kernel_2.h5' #r'./weights/cnnTf2Kernel.h5' model.load_weights(file) test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc,'loss=',test_loss) testImg = x_test[6] testLabel = y_test[6] print('testLabel:', testLabel) heatmap = getHeatMap(model, testImg) # Display heatmap plt.matshow(heatmap) plt.show() getCombineImg(testImg,heatmap, r'./res/7.png')