152 lines
5.2 KiB
Python
152 lines
5.2 KiB
Python
import numpy as np
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import random
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import theano
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from keras.models import Sequential
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from keras.callbacks import Callback
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.regularizers import l2
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from keras.layers.convolutional import Convolution2D, MaxPooling2D
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from keras.utils import np_utils
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from keras.datasets import mnist
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from matplotlib import pyplot as plt
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from matplotlib import animation
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nb_classes = 10
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batch_size = 128
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nb_epoch = 10
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max_train_samples = 5000
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max_test_samples = 1
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np.random.seed(1337)
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# the data, shuffled and split between tran and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(-1,1,28,28)[:max_train_samples]
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X_train = X_train.astype("float32")
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X_train /= 255
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X_test = X_test.reshape(-1,1,28,28)[:max_test_samples]
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X_test = X_test.astype("float32")
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X_test /= 255
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# convert class vectors to binary class matrices
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Y_train = np_utils.to_categorical(y_train, nb_classes)[:max_train_samples]
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class Frames(object):
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def __init__(self, n_plots=16):
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self._n_frames = 0
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self._framedata = []
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self._titles = []
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for i in range(n_plots):
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self._framedata.append([])
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def add_frame(self, i, frame):
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self._framedata[i].append(frame)
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def set_title(self, title):
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self._titles.append(title)
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class SubplotTimedAnimation(animation.TimedAnimation):
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def __init__(self, fig, frames, grid=(4, 4), interval=10, blit=False, **kwargs):
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self.n_plots = grid[0] * grid[1]
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self.axes = [fig.add_subplot(grid[0], grid[1], i + 1) for i in range(self.n_plots)]
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for axis in self.axes:
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axis.get_xaxis().set_ticks([])
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axis.get_yaxis().set_ticks([])
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self.frames = frames
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self.imgs = [self.axes[i].imshow(frames._framedata[i][0], interpolation='nearest', cmap='bone') for i in range(self.n_plots)]
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self.title = fig.suptitle('')
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super(SubplotTimedAnimation, self).__init__(fig, interval=interval, blit=blit, **kwargs)
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def _draw_frame(self, j):
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for i in range(self.n_plots):
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self.imgs[i].set_data(self.frames._framedata[i][j])
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if len(self.frames._titles) > j:
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self.title.set_text(self.frames._titles[j])
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self._drawn_artists = self.imgs
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def new_frame_seq(self):
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return iter(range(len(self.frames._framedata[0])))
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def _init_draw(self):
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for img in self.imgs:
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img.set_data([[]])
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def combine_imgs(imgs, grid=(1,1)):
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n_imgs, img_h, img_w = imgs.shape
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if n_imgs != grid[0] * grid[1]:
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raise ValueError()
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combined = np.zeros((grid[0] * img_h, grid[1] * img_w))
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for i in range(grid[0]):
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for j in range(grid[1]):
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combined[img_h*i:img_h*(i+1),img_w*j:img_w*(j+1)] = imgs[grid[0] * i + j]
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return combined
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class DrawActivations(Callback):
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def __init__(self, figsize):
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self.fig = plt.figure(figsize=figsize)
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def on_train_begin(self):
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self.imgs = Frames(n_plots=5)
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layers_0_ids = np.random.choice(32, 16, replace=False)
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self.test_layer0 = theano.function([self.model.get_input()], self.model.layers[1].get_output(train=False)[0, layers_0_ids])
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layers_1_ids = np.random.choice(64, 36, replace=False)
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self.test_layer1 = theano.function([self.model.get_input()], self.model.layers[5].get_output(train=False)[0, layers_1_ids])
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self.test_layer2 = theano.function([self.model.get_input()], self.model.layers[10].get_output(train=False)[0])
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def on_epoch_begin(self, epoch):
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self.epoch = epoch
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def on_batch_end(self, batch):
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if batch % 5 == 0:
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self.imgs.add_frame(0, X_test[0,0])
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self.imgs.add_frame(1, combine_imgs(self.test_layer0(X_test), grid=(4, 4)))
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self.imgs.add_frame(2, combine_imgs(self.test_layer1(X_test), grid=(6, 6)))
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self.imgs.add_frame(3, self.test_layer2(X_test).reshape((16,16)))
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self.imgs.add_frame(4, self.model._predict(X_test)[0].reshape((1,10)))
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self.imgs.set_title('Epoch #%d - Batch #%d' % (self.epoch, batch))
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def on_train_end(self):
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anim = SubplotTimedAnimation(self.fig, self.imgs, grid=(1,5), interval=10, blit=False, repeat_delay=1000)
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# anim.save('test_gif.gif', fps=15, writer='imagemagick')
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plt.show()
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# model = Sequential()
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# model.add(Dense(784, 50))
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# model.add(Activation('relu'))
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# model.add(Dense(50, 10))
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# model.add(Activation('softmax'))
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model = Sequential()
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model.add(Convolution2D(32, 1, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(poolsize=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(64*8*8, 256))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(256, 10, W_regularizer = l2(0.1)))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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# Fit the model
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draw_weights = DrawActivations(figsize=(5.4, 1.35))
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model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, callbacks=[draw_weights])
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