def _updated_config(self):
"""Shared between different serialization methods."""
from keras import __version__ as keras_version
config = self.get_config()
model_config = {
'class_name': self.__class__.__name__,
'config': config,
'keras_version': keras_version
}
return model_config
python类__version__()的实例源码
def homepage():
welcomeLabel = """
<h1>Welcome at Iris classification by Gabor Vecsei!</h1>
<h4>Keras ver: {0}</h4>
""".format(keras.__version__)
return welcomeLabel
def _updated_config(self):
'''shared between different serialization methods'''
from keras import __version__ as keras_version
config = self.get_config()
model_config = {
'class_name': self.__class__.__name__,
'config': config,
'keras_version': keras_version
}
return model_config
test_conv2d_model_tensorflow_ordering.py 文件源码
项目：deeplift
作者: kundajelab
项目源码
文件源码
阅读 26
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def setUp(self):
if (hasattr(keras, '__version__')==False):
self.keras_version = 0.2 #didn't have the __version__ tag
else:
self.keras_version = float(keras.__version__[0:2])
if (self.keras_version <= 0.2):
pass
else:
self.inp = (np.random.randn(10*10*51*51)
.reshape(10,10,51,51).transpose(0,2,3,1))
self.keras_model = keras.models.Sequential()
conv_layer = keras.layers.convolutional.Convolution2D(
nb_filter=2, nb_row=4, nb_col=4, subsample=(2,2),
activation="relu", input_shape=(51,51,10),
dim_ordering='tf')
self.keras_model.add(conv_layer)
self.keras_model.add(keras.layers.convolutional.MaxPooling2D(
pool_size=(4,4), strides=(2,2),
dim_ordering='tf'))
self.keras_model.add(keras.layers.convolutional.AveragePooling2D(
pool_size=(4,4), strides=(2,2),
dim_ordering='tf'))
self.keras_model.add(keras.layers.core.Flatten())
self.keras_model.add(keras.layers.core.Dense(output_dim=1))
self.keras_model.add(keras.layers.core.Activation("sigmoid"))
self.keras_model.compile(loss="mse", optimizer="sgd")
if (self.keras_version <= 0.3):
self.keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input],
self.keras_model.layers[-1].get_output(False))
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].get_output(False)[:,0]),
self.keras_model.layers[0].input)
self.grad_func = theano.function(
[self.keras_model.layers[0].input],
grad, allow_input_downcast=True,
on_unused_input='ignore')
else:
keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
self.keras_model.layers[-1].output)
self.keras_output_fprop_func =\
lambda x: keras_output_fprop_func(x,False)
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].output[:,0]),
self.keras_model.layers[0].input)
grad_func = theano.function(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
grad, allow_input_downcast=True,
on_unused_input='ignore')
self.grad_func = lambda x: grad_func(x, False)
def setUp(self):
if (hasattr(keras, '__version__')==False):
self.keras_version = 0.2 #didn't have the __version__ tag
else:
self.keras_version = float(keras.__version__[0:3])
self.inp = (np.random.randn(10*10*51)
.reshape(10,10,51).transpose(0,2,1))
self.keras_model = keras.models.Sequential()
conv_layer = keras.layers.convolutional.Convolution1D(
nb_filter=2, filter_length=4, subsample_length=2,
#re. input_shape=(51,10), that is, putting the channel
#axis last; this is actually due to the bug
#that seems to date back to v0.2.0...
#https://github.com/fchollet/keras/blob/0.2.0/keras/layers/convolutional.py#L88
activation="relu", input_shape=(51,10))
self.keras_model.add(conv_layer)
self.keras_model.add(keras.layers.convolutional.MaxPooling1D(
pool_length=4, stride=2))
if (self.keras_version > 0.2):
self.keras_model.add(keras.layers.convolutional.AveragePooling1D(
pool_length=4, stride=2))
else:
pass #there was no average pooling in 0.2.0 it seems
self.keras_model.add(keras.layers.core.Flatten())
self.keras_model.add(keras.layers.core.Dense(output_dim=1))
self.keras_model.add(keras.layers.core.Activation("sigmoid"))
self.keras_model.compile(loss="mse", optimizer="sgd")
if (self.keras_version <= 0.3):
self.keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input],
self.keras_model.layers[-1].get_output(False))
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].get_output(False)[:,0]),
self.keras_model.layers[0].input)
self.grad_func = theano.function(
[self.keras_model.layers[0].input],
grad, allow_input_downcast=True)
else:
keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
self.keras_model.layers[-1].output)
self.keras_output_fprop_func =\
lambda x: keras_output_fprop_func(x,False)
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].output[:,0]),
self.keras_model.layers[0].input)
grad_func = theano.function(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
grad, allow_input_downcast=True,
on_unused_input='ignore')
self.grad_func = lambda x: grad_func(x, False)
def setUp(self):
if (hasattr(keras, '__version__')==False):
self.keras_version = 0.2 #didn't have the __version__ tag
else:
self.keras_version = float(keras.__version__[0:3])
self.inp = (np.random.randn(10*10*51*51)
.reshape(10,10,51,51))
self.keras_model = keras.models.Sequential()
conv_layer = keras.layers.convolutional.Convolution2D(
nb_filter=2, nb_row=4, nb_col=4, subsample=(2,2),
activation="relu", input_shape=(10,51,51),
dim_ordering='th')
self.keras_model.add(conv_layer)
if (self.keras_version > 0.2):
self.keras_model.add(keras.layers.convolutional.MaxPooling2D(
pool_size=(4,4), strides=(2,2),
dim_ordering='th'))
self.keras_model.add(keras.layers.convolutional.AveragePooling2D(
pool_size=(4,4), strides=(2,2),
dim_ordering='th'))
else:
print(self.keras_version)
self.keras_model.add(keras.layers.convolutional.MaxPooling2D(
pool_size=(4,4), stride=(2,2)))
#There is no average pooling in version 0.2.0
self.keras_model.add(keras.layers.core.Flatten())
self.keras_model.add(keras.layers.core.Dense(output_dim=1))
self.keras_model.add(keras.layers.core.Activation("sigmoid"))
self.keras_model.compile(loss="mse", optimizer="sgd")
if (self.keras_version <= 0.3):
self.keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input],
self.keras_model.layers[-1].get_output(False))
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].get_output(False)[:,0]),
self.keras_model.layers[0].input)
self.grad_func = theano.function(
[self.keras_model.layers[0].input],
grad, allow_input_downcast=True,
on_unused_input='ignore')
else:
keras_output_fprop_func = compile_func(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
self.keras_model.layers[-1].output)
self.keras_output_fprop_func =\
lambda x: keras_output_fprop_func(x,False)
grad = theano.grad(theano.tensor.sum(
self.keras_model.layers[-2].output[:,0]),
self.keras_model.layers[0].input)
grad_func = theano.function(
[self.keras_model.layers[0].input,
keras.backend.learning_phase()],
grad, allow_input_downcast=True,
on_unused_input='ignore')
self.grad_func = lambda x: grad_func(x, False)
def setUp(self):
if (hasattr(keras, '__version__')==False):
self.keras_version = 0.2 #didn't have the __version__ tag
else:
self.keras_version = float(keras.__version__[0:3])
self.inp1 = (np.random.randn(10*10*51)
.reshape(10,10,51).transpose(0,2,1))
self.inp2 = (np.random.randn(10*10*51)
.reshape(10,10,51).transpose(0,2,1))
self.run_functional_tests = True
if (self.keras_version < 1.0):
self.run_functional_tests = False
return #skip setup
inp1 = keras.layers.Input(shape=(51,10), name="inp1")
inp2 = keras.layers.Input(shape=(51,10), name="inp2")
conv = keras.layers.convolutional.Convolution1D(
nb_filter=2, filter_length=4,
subsample_length=2, activation="relu")
maxpool = keras.layers.convolutional.MaxPooling1D(pool_length=4, stride=2)
conv1_out = conv(inp1)
conv2_out = conv(inp2)
maxpool1_out = maxpool(conv1_out)
maxpool2_out = maxpool(conv2_out)
merge_out = keras.layers.merge([maxpool1_out, maxpool2_out],
mode='concat', concat_axis=2)
flatten_out = keras.layers.core.Flatten()(merge_out)
dense1_out = keras.layers.core.Dense(output_dim=5)(flatten_out)
dense1relu_out = keras.layers.core.Activation("relu")(dense1_out)
output_preact = keras.layers.core.Dense(
output_dim=1, name="output_preact")(dense1relu_out)
output = keras.layers.core.Activation("sigmoid",
name="output_postact")(output_preact)
self.keras_model = keras.models.Model(input=[inp1, inp2],
output=output)
self.keras_model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
if (self.keras_version <= 0.3):
pass
else:
keras_output_fprop_func = compile_func(
[inp1, inp2, keras.backend.learning_phase()],
self.keras_model.layers[-1].output)
self.keras_output_fprop_func =\
lambda x,y: keras_output_fprop_func(x,y,False)
grad = theano.grad(theano.tensor.sum(output_preact[:,0]),
[inp1, inp2])
grad_func = theano.function(
[inp1, inp2, keras.backend.learning_phase()],
grad, allow_input_downcast=True, on_unused_input='ignore')
self.grad_func = lambda x,y: grad_func(x,y,False)
