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Fix examples and applications to keras-2

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This commit is contained in:
Junwei Pan
2017-03-31 08:09:11 -07:00
parent db019c6034
commit 3c1594b967
5 changed files with 52 additions and 56 deletions
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examples/cifar10_ror.py
+4 -4
View File
@@ -36,19 +36,19 @@ generator = ImageDataGenerator(rotation_range=15,
generator.fit(trainX, seed=0)
model = ResidualOfResidual(depth=40, width=2, dropout_rate=0.0, weights='None')
model = ResidualOfResidual(depth=40, width=2, dropout_rate=0.0, weights=None)
optimizer = Adam(lr=1e-3)
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["acc"])
print("Finished compiling")
model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), samples_per_epoch=len(trainX),
nb_epoch=nb_epoch,
model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
epochs=nb_epoch,
callbacks=[callbacks.ModelCheckpoint("weights/RoR-WRN-40-2-Weights.h5", monitor="val_acc",
save_best_only=True, save_weights_only=True)],
validation_data=(testX, testY),
nb_val_samples=testX.shape[0], verbose=2)
verbose=2)
scores = model.evaluate(testX, testY, batch_size)
print("Test loss : ", scores[0])
examples/cifar10_wide_resnet.py
+3 -4
View File
@@ -47,13 +47,12 @@ model.summary()
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
print("Finished compiling")
model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), samples_per_epoch=len(trainX),
nb_epoch=nb_epoch,
model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size,
epochs=nb_epoch,
callbacks=[
callbacks.ModelCheckpoint("WRN-28-8 Weights.h5", monitor="val_acc", save_best_only=True,
save_weights_only=True)],
validation_data=(testX, testY),
nb_val_samples=testX.shape[0], )
validation_data=(testX, testY))
scores = model.evaluate(testX, testY, batch_size)
print("Test loss : %0.5f" % (scores[0]))
keras_contrib/applications/densenet.py
-2
View File
@@ -405,8 +405,6 @@ def __transition_up_block(ip, nb_filters, type='upsampling', output_shape=None,
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3), padding="same", kernel_regularizer=l2(weight_decay), activation='relu',
use_bias=False, kernel_initializer='he_uniform')(ip)
# x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
# bias=False, init='he_uniform')(ip)
x = SubPixelUpscaling(scale_factor=2)(x)
x = Conv2D(nb_filters, (3, 3), activation="relu", padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(x)
keras_contrib/applications/ror.py
+26 -27
View File
@@ -14,9 +14,9 @@ import warnings
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D, MaxPooling2D
from keras.layers import Input, merge
from keras.layers.merge import add
from keras.layers import Input, Conv2D
from keras.layers.normalization import BatchNormalization
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
@@ -70,7 +70,6 @@ def ResidualOfResidual(depth=40, width=2, dropout_rate=0.0,
# Returns
A Keras model instance.
"""
if weights not in {'cifar10', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `cifar10` '
@@ -88,7 +87,7 @@ def ResidualOfResidual(depth=40, width=2, dropout_rate=0.0,
input_shape = _obtain_input_shape(input_shape,
default_size=32,
min_size=8,
dim_ordering=K.image_dim_ordering(),
data_format=K.image_dim_ordering(),
include_top=include_top)
if input_tensor is None:
@@ -164,22 +163,22 @@ def __initial_conv_block(input, k=1, dropout=0.0, initial=False):
# Check if input number of filters is same as 16 * k, else create convolution2d for this input
if initial:
if K.image_dim_ordering() == "th":
init = Convolution2D(16 * k, 1, 1, init='he_normal', border_mode='same')(init)
init = Conv2D(16 * k, (1, 1), kernel_initializer='he_normal', padding='same')(init)
else:
init = Convolution2D(16 * k, 1, 1, init='he_normal', border_mode='same')(init)
init = Conv2D(16 * k, (1, 1), kernel_initializer='he_normal', padding='same')(init)
x = BatchNormalization(axis=channel_axis)(input)
x = Activation('relu')(x)
x = Convolution2D(16 * k, 3, 3, border_mode='same', init='he_normal')(x)
x = Conv2D(16 * k, (3, 3), padding='same', kernel_initializer='he_normal')(x)
if dropout > 0.0:
x = Dropout(dropout)(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
x = Convolution2D(16 * k, 3, 3, border_mode='same', init='he_normal')(x)
x = Conv2D(16 * k, (3, 3), padding='same', kernel_initializer='he_normal')(x)
m = merge([init, x], mode='sum')
m = add([init, x])
return m
@@ -191,23 +190,23 @@ def __conv_block(input, nb_filters=32, k=1, dropout=0.0):
# Check if input number of filters is same as 32 * k, else create convolution2d for this input
if K.image_dim_ordering() == "th":
if init._keras_shape[1] != nb_filters * k:
init = Convolution2D(nb_filters * k, 1, 1, init='he_normal', border_mode='same')(init)
init = Conv2D(nb_filters * k, (1, 1), kernel_initializer='he_normal', padding='same')(init)
else:
if init._keras_shape[-1] != nb_filters * k:
init = Convolution2D(nb_filters * k, 1, 1, init='he_normal', border_mode='same')(init)
init = Conv2D(nb_filters * k, (1, 1), kernel_initializer='he_normal', padding='same')(init)
x = BatchNormalization(axis=channel_axis)(input)
x = Activation('relu')(x)
x = Convolution2D(nb_filters * k, 3, 3, border_mode='same', init='he_normal')(x)
x = Conv2D(nb_filters * k, (3, 3), padding='same', kernel_initializer='he_normal')(x)
if dropout > 0.0:
x = Dropout(dropout)(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
x = Convolution2D(nb_filters * k, 3, 3, border_mode='same', init='he_normal')(x)
x = Conv2D(nb_filters * k, (3, 3), padding='same', kernel_initializer='he_normal')(x)
m = merge([init, x], mode='sum')
m = add([init, x])
return m
@@ -238,46 +237,46 @@ def __create_pre_residual_of_residual(nb_classes, img_input, include_top, depth=
channel_axis = 1 if K.image_dim_ordering() == "th" else -1
# Initial convolution layer
x = Convolution2D(16, 3, 3, border_mode='same', init='he_normal')(img_input)
x = Conv2D(16, (3, 3), padding='same', kernel_initializer='he_normal')(img_input)
nb_conv = 4 # Dont count 4 long residual connections in WRN models
conv0_level1_shortcut = Convolution2D(64 * width, 1, 1, border_mode='same', subsample=(4, 4),
name='conv0_level1_shortcut')(x)
conv0_level1_shortcut = Conv2D(64 * width, (1, 1), padding='same', strides=(4, 4),
name='conv0_level1_shortcut')(x)
conv1_level2_shortcut = Convolution2D(16 * width, 1, 1, border_mode='same',
name='conv1_level2_shortcut')(x)
conv1_level2_shortcut = Conv2D(16 * width, (1, 1), padding='same',
name='conv1_level2_shortcut')(x)
for i in range(N):
initial = (i == 0)
x = __initial_conv_block(x, k=width, dropout=dropout, initial=initial)
nb_conv += 2
# Add Level 2 shortcut
x = merge([x, conv1_level2_shortcut], mode='sum')
x = add([x, conv1_level2_shortcut])
x = MaxPooling2D((2, 2))(x)
conv2_level2_shortcut = Convolution2D(32 * width, 1, 1, border_mode='same',
name='conv2_level2_shortcut')(x)
conv2_level2_shortcut = Conv2D(32 * width, (1, 1), padding='same',
name='conv2_level2_shortcut')(x)
for i in range(N):
x = __conv_block(x, k=width, dropout=dropout)
nb_conv += 2
# Add Level 2 shortcut
x = merge([x, conv2_level2_shortcut], mode='sum')
x = add([x, conv2_level2_shortcut])
x = MaxPooling2D((2, 2))(x)
conv3_level2_shortcut = Convolution2D(64 * width, 1, 1, border_mode='same',
name='conv3_level2_shortcut')(x)
conv3_level2_shortcut = Conv2D(64 * width, (1, 1), padding='same',
name='conv3_level2_shortcut')(x)
for i in range(N):
x = __conv_block(x, nb_filters=64, k=width, dropout=dropout)
nb_conv += 2
# Add Level 2 shortcut
x = merge([x, conv3_level2_shortcut], mode='sum')
x = add([x, conv3_level2_shortcut])
# Add Level 1 shortcut
x = merge([x, conv0_level1_shortcut], mode='sum')
x = add([x, conv0_level1_shortcut])
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
keras_contrib/applications/wide_resnet.py
+19 -19
View File
@@ -14,9 +14,9 @@ import warnings
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D, MaxPooling2D
from keras.layers import Input, merge
from keras.layers import Input, merge, Conv2D
from keras.layers.merge import add
from keras.layers.normalization import BatchNormalization
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
@@ -88,7 +88,7 @@ def WideResidualNetwork(depth=28, width=8, dropout_rate=0.0,
input_shape = _obtain_input_shape(input_shape,
default_size=32,
min_size=8,
dim_ordering=K.image_dim_ordering(),
data_format=K.image_dim_ordering(),
include_top=include_top)
if input_tensor is None:
@@ -157,7 +157,7 @@ def WideResidualNetwork(depth=28, width=8, dropout_rate=0.0,
def __conv1_block(input):
x = Convolution2D(16, 3, 3, border_mode='same')(input)
x = Conv2D(16, (3, 3), padding='same')(input)
channel_axis = 1 if K.image_dim_ordering() == "th" else -1
@@ -174,23 +174,23 @@ def __conv2_block(input, k=1, dropout=0.0):
# Check if input number of filters is same as 16 * k, else create convolution2d for this input
if K.image_dim_ordering() == "th":
if init._keras_shape[1] != 16 * k:
init = Convolution2D(16 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(16 * k, (1, 1), activation='linear', padding='same')(init)
else:
if init._keras_shape[-1] != 16 * k:
init = Convolution2D(16 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(16 * k, (1, 1), activation='linear', padding='same')(init)
x = Convolution2D(16 * k, 3, 3, border_mode='same')(input)
x = Conv2D(16 * k, (3, 3), padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0:
x = Dropout(dropout)(x)
x = Convolution2D(16 * k, 3, 3, border_mode='same')(x)
x = Conv2D(16 * k, (3, 3), padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
m = merge([init, x], mode='sum')
m = add([init, x])
return m
@@ -202,23 +202,23 @@ def __conv3_block(input, k=1, dropout=0.0):
# Check if input number of filters is same as 32 * k, else create convolution2d for this input
if K.image_dim_ordering() == "th":
if init._keras_shape[1] != 32 * k:
init = Convolution2D(32 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(32 * k, (1, 1), activation='linear', padding='same')(init)
else:
if init._keras_shape[-1] != 32 * k:
init = Convolution2D(32 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(32 * k, (1, 1), activation='linear', padding='same')(init)
x = Convolution2D(32 * k, 3, 3, border_mode='same')(input)
x = Conv2D(32 * k, (3, 3), padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0:
x = Dropout(dropout)(x)
x = Convolution2D(32 * k, 3, 3, border_mode='same')(x)
x = Conv2D(32 * k, (3, 3), padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
m = merge([init, x], mode='sum')
m = add([init, x])
return m
@@ -230,23 +230,23 @@ def ___conv4_block(input, k=1, dropout=0.0):
# Check if input number of filters is same as 64 * k, else create convolution2d for this input
if K.image_dim_ordering() == "th":
if init._keras_shape[1] != 64 * k:
init = Convolution2D(64 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(64 * k, (1, 1), activation='linear', padding='same')(init)
else:
if init._keras_shape[-1] != 64 * k:
init = Convolution2D(64 * k, 1, 1, activation='linear', border_mode='same')(init)
init = Conv2D(64 * k, (1, 1), activation='linear', padding='same')(init)
x = Convolution2D(64 * k, 3, 3, border_mode='same')(input)
x = Conv2D(64 * k, (3, 3), padding='same')(input)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
if dropout > 0.0:
x = Dropout(dropout)(x)
x = Convolution2D(64 * k, 3, 3, border_mode='same')(x)
x = Conv2D(64 * k, (3, 3), padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
m = merge([init, x], mode='sum')
m = add([init, x])
return m