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Finish PR backporting

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This commit is contained in:
Francois Chollet 2016-04-04 11:30:24 -07:00
parent eb8b40cccd
commit 2902149f77
6 changed files with 19 additions and 13 deletions
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2
examples/antirectifier.py
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@ -63,7 +63,7 @@ batch_size = 128
nb_classes = 10
nb_epoch = 40
# the data, shuffled and split between tran and test sets
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)

2
examples/mnist_cnn.py
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@ -28,7 +28,7 @@ nb_pool = 2
# convolution kernel size
nb_conv = 3
# the data, shuffled and split between tran and test sets
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)

2
examples/mnist_mlp.py
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@ -20,7 +20,7 @@ batch_size = 128
nb_classes = 10
nb_epoch = 20
# the data, shuffled and split between tran and test sets
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)

2
examples/mnist_siamese_graph.py
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@ -73,7 +73,7 @@ def compute_accuracy(predictions, labels):
return labels[predictions.ravel() < 0.5].mean()
# the data, shuffled and split between tran and test sets
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)

7
keras/engine/training.py
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@ -3,6 +3,7 @@
from __future__ import print_function
from __future__ import absolute_import
import warnings
import copy
import time
import numpy as np
@ -1170,6 +1171,12 @@ class Model(Container):
samples_seen += batch_size
# epoch finished
if samples_seen > samples_per_epoch:
warnings.warn('Epoch comprised more than '
'`samples_per_epoch` samples, '
'which might affect learning results. '
'Set `samples_per_epoch` correctly '
'to avoid this warning.')
if samples_seen >= samples_per_epoch and do_validation:
if val_gen:
val_outs = self.evaluate_generator(validation_data,

17
keras/preprocessing/image.py
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@ -11,14 +11,13 @@ from scipy import linalg
from os import listdir
from os.path import isfile, join
import random
import math
from six.moves import range
import threading
def random_rotation(x, rg, fill_mode='nearest', cval=0.):
angle = random.uniform(-rg, rg)
angle = np.random.uniform(-rg, rg)
x = ndimage.interpolation.rotate(x, angle,
axes=(1, 2),
reshape=False,
@ -31,9 +30,9 @@ def random_shift(x, wrg, hrg, fill_mode='nearest', cval=0.):
shift_x = shift_y = 0
if wrg:
shift_x = random.uniform(-wrg, wrg) * x.shape[2]
shift_x = np.random.uniform(-wrg, wrg) * x.shape[2]
if hrg:
shift_y = random.uniform(-hrg, hrg) * x.shape[1]
shift_y = np.random.uniform(-hrg, hrg) * x.shape[1]
x = ndimage.interpolation.shift(x, (0, shift_y, shift_x),
order=0,
mode=fill_mode,
@ -59,7 +58,7 @@ def random_barrel_transform(x, intensity):
def random_shear(x, intensity, fill_mode='nearest', cval=0.):
shear = random.uniform(-intensity, intensity)
shear = np.random.uniform(-intensity, intensity)
shear_matrix = np.array([[1.0, -math.sin(shear), 0.0],
[0.0, math.cos(shear), 0.0],
[0.0, 0.0, 1.0]])
@ -76,8 +75,8 @@ def random_channel_shift(x, rg):
def random_zoom(x, rg, fill_mode='nearest', cval=0.):
zoom_w = random.uniform(1.-rg, 1.)
zoom_h = random.uniform(1.-rg, 1.)
zoom_w = np.random.uniform(1.-rg, 1.)
zoom_h = np.random.uniform(1.-rg, 1.)
x = ndimage.interpolation.zoom(x, zoom=(1., zoom_w, zoom_h),
mode=fill_mode,
cval=cval)
@ -253,10 +252,10 @@ class ImageDataGenerator(object):
if self.width_shift_range or self.height_shift_range:
x = random_shift(x, self.width_shift_range, self.height_shift_range)
if self.horizontal_flip:
if random.random() < 0.5:
if np.random.random() < 0.5:
x = horizontal_flip(x)
if self.vertical_flip:
if random.random() < 0.5:
if np.random.random() < 0.5:
x = vertical_flip(x)
if self.shear_range:
x = random_shear(x, self.shear_range)