CNN 可视化示例
For keras < v1
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import os os.environ['THEANO_FLAGS']='mode=FAST_RUN,device=gpu,floatX=float32' # This gives a 'perform' error in compile #os.environ['THEANO_FLAGS']='mode=FAST_COMPILE,device=gpu1,floatX=float32' #os.environ['THEANO_FLAGS']='device=gpu0'
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import theano print theano.config.device
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from __future__ import absolute_import from __future__ import print_function import pylab as pl import matplotlib.cm as cm import numpy as np np.random.seed(1337) # for reproducibility from keras.datasets import mnist from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.utils import np_utils ''' Train a simple convnet on the MNIST dataset. Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py Get to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. '''
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np.set_printoptions(precision=5, suppress=True)
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%matplotlib inline
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nb_classes = 10 # the data, shuffled and split between tran and test sets (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(X_train.shape[0], 1, 28, 28) X_test = X_test.reshape(X_test.shape[0], 1, 28, 28) X_train = X_train.astype("float32") X_test = X_test.astype("float32") X_train /= 255 X_test /= 255 print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') # convert class vectors to binary class matrices Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes)
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i = 4600 pl.imshow(X_train[i, 0], interpolation='nearest', cmap=cm.binary) print("label : ", Y_train[i,:])
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model = Sequential() model.add(Convolution2D(32, 1, 3, 3, border_mode='full')) convout1 = Activation('relu') model.add(convout1) model.add(Convolution2D(32, 32, 3, 3)) convout2 = Activation('relu') model.add(convout2) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(32*196, 128)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(128, nb_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta')
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WEIGHTS_FNAME = 'mnist_cnn_weights.hdf' if True and os.path.exists(WEIGHTS_FNAME): # Just change the True to false to force re-training print('Loading existing weights') model.load_weights(WEIGHTS_FNAME) else: batch_size = 128 nb_epoch = 12 model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1, validation_data=(X_test, Y_test)) model.save_weights(WEIGHTS_FNAME) score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0) print('Test score:', score[0]) print('Test accuracy:', score[1])
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print(model.predict(X_test[1:5])) print(Y_test[1:5])
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Y_pred = model.predict(X_test) # Convert one-hot to index y_pred = np.argmax(Y_pred, axis=1)
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from sklearn.metrics import classification_report print(classification_report(y_test, y_pred))
Convolution visualizations
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convout1_f = theano.function([model.get_input(train=False)], convout1.get_output(train=False)) #convout2_f = theano.function([model.get_input(train=False)], convout2.get_output(train=False))
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# utility functions
from mpl_toolkits.axes_grid1 import make_axes_locatable def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None): """Wrapper around pl.imshow""" if cmap is None: cmap = cm.jet if vmin is None: vmin = data.min() if vmax is None: vmax = data.max() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap) pl.colorbar(im, cax=cax)
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i = 4600 # Visualize the first layer of convolutions on an input image X = X_test[i:i+1] pl.figure() pl.title('input') nice_imshow(pl.gca(), np.squeeze(X), vmin=0, vmax=1, cmap=cm.binary)
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import numpy.ma as ma def make_mosaic(imgs, nrows, ncols, border=1): """ Given a set of images with all the same shape, makes a mosaic with nrows and ncols """ nimgs = imgs.shape[0] imshape = imgs.shape[1:] mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border, ncols * imshape[1] + (ncols - 1) * border), dtype=np.float32) paddedh = imshape[0] + border paddedw = imshape[1] + border for i in xrange(nimgs): row = int(np.floor(i / ncols)) col = i % ncols mosaic[row * paddedh:row * paddedh + imshape[0], col * paddedw:col * paddedw + imshape[1]] = imgs[i] return mosaic #pl.imshow(make_mosaic(np.random.random((9, 10, 10)), 3, 3, border=1))
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# Visualize weights
W = model.layers[0].W.get_value(borrow=True) W = np.squeeze(W) print("W shape : ", W.shape) pl.figure(figsize=(15, 15)) pl.title('conv1 weights') nice_imshow(pl.gca(), make_mosaic(W, 6, 6), cmap=cm.binary)
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# Visualize convolution result (after activation)
C1 = convout1_f(X) C1 = np.squeeze(C1) print("C1 shape : ", C1.shape) pl.figure(figsize=(15, 15)) pl.suptitle('convout1') nice_imshow(pl.gca(), make_mosaic(C1, 6, 6), cmap=cm.binary)