CNN 可视化示例
For keras < v1
In [1]:
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‘
In [2]:
import theano print theano.config.device
Couldn‘t import dot_parser, loading of dot files will not be possible. gpu
Using gpu device 0: Tesla M2075
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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.
‘‘‘
Out[3]:
‘\n Train a simple convnet on the MNIST dataset.\n\n Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py\n\n Get to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning).\n 16 seconds per epoch on a GRID K520 GPU.\n‘
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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)
X_train shape: (60000, 1, 28, 28) 60000 train samples 10000 test samples
In [7]:
i = 4600
pl.imshow(X_train[i, 0], interpolation=‘nearest‘, cmap=cm.binary)
print("label : ", Y_train[i,:])
label : [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
In [8]:
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])
Train on 60000 samples, validate on 10000 samples Epoch 0 60000/60000 [==============================] - 79s - loss: 0.2596 - acc: 0.9200 - val_loss: 0.0548 - val_acc: 0.9826 Epoch 1 60000/60000 [==============================] - 79s - loss: 0.0961 - acc: 0.9713 - val_loss: 0.0441 - val_acc: 0.9861 Epoch 2 60000/60000 [==============================] - 79s - loss: 0.0735 - acc: 0.9782 - val_loss: 0.0426 - val_acc: 0.9860 Epoch 3 60000/60000 [==============================] - 79s - loss: 0.0617 - acc: 0.9816 - val_loss: 0.0330 - val_acc: 0.9885 Epoch 4 60000/60000 [==============================] - 79s - loss: 0.0513 - acc: 0.9844 - val_loss: 0.0277 - val_acc: 0.9913 Epoch 5 60000/60000 [==============================] - 79s - loss: 0.0467 - acc: 0.9859 - val_loss: 0.0305 - val_acc: 0.9895 Epoch 6 60000/60000 [==============================] - 79s - loss: 0.0416 - acc: 0.9868 - val_loss: 0.0291 - val_acc: 0.9911 Epoch 7 60000/60000 [==============================] - 79s - loss: 0.0377 - acc: 0.9882 - val_loss: 0.0289 - val_acc: 0.9909 Epoch 8 60000/60000 [==============================] - 79s - loss: 0.0349 - acc: 0.9889 - val_loss: 0.0263 - val_acc: 0.9914 Epoch 9 60000/60000 [==============================] - 79s - loss: 0.0333 - acc: 0.9900 - val_loss: 0.0258 - val_acc: 0.9916 Epoch 10 60000/60000 [==============================] - 79s - loss: 0.0305 - acc: 0.9906 - val_loss: 0.0240 - val_acc: 0.9924 Epoch 11 60000/60000 [==============================] - 79s - loss: 0.0273 - acc: 0.9918 - val_loss: 0.0299 - val_acc: 0.9906 Test score: 0.0299028589356 Test accuracy: 0.9906
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print(model.predict(X_test[1:5])) print(Y_test[1:5])
[[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]] [[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.] [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.] [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]
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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))
precision recall f1-score support
0 0.99 0.99 0.99 980
1 0.99 1.00 1.00 1135
2 0.99 0.99 0.99 1032
3 0.98 1.00 0.99 1010
4 0.99 0.99 0.99 982
5 0.99 0.99 0.99 892
6 0.99 0.99 0.99 958
7 0.99 0.99 0.99 1028
8 1.00 0.98 0.99 974
9 0.99 0.98 0.99 1009
avg / total 0.99 0.99 0.99 10000
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)
W shape : (32, 3, 3)
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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)
C1 shape : (32, 30, 30)
时间： 2024-11-05 12:10:53