def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2**30))
self.x1 = T.tensor3('x1')
self.x2 = T.tensor3('x2')
self.x1_bis = T.matrix('x1_bis')
self.x2_bis = T.matrix('x2_bis')
self.y = T.vector('y') #0 or 1
self.z = T.vector('z') #-1 or 1
self.model1 = NN_layers.doubleDotMeanLayer(self.numpy_rng,
self.theano_rng,
self.x1,
self.x1_bis,
INPUT_SHAPE,
n_out=OUTPUT_SHAPE[1],
W=None)
self.model2 = NN_layers.doubleDotMeanLayer(self.numpy_rng,
self.theano_rng,
self.x2,
self.x2_bis,
INPUT_SHAPE,
n_out=OUTPUT_SHAPE[1],
W=self.model1.W)
self.params = []
self.params.extend(self.model1.params)
def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2**30))
self.x1 = T.tensor3('x1')
self.x2 = T.tensor3('x2')
self.l1 = T.vector('l1')
self.l2 = T.vector('l2')
self.y = T.vector('y') #0 or 1
self.z = T.vector('z') #-1 or 1
self.model1 = NN_layers.exponentialWeightsDotMeanLayer(
self.numpy_rng,
self.theano_rng,
self.x1,
INPUT_SHAPE,
self.l1,
max_length=35,
n_out=OUTPUT_SHAPE[1],
batch_size=BATCH_SIZE,
W=None)
self.model2 = NN_layers.exponentialWeightsDotMeanLayer(
self.numpy_rng,
self.theano_rng,
self.x2,
INPUT_SHAPE,
self.l2,
max_length=35,
n_out=OUTPUT_SHAPE[1],
batch_size=BATCH_SIZE,
W=self.model1.W)
self.params = []
self.params.extend(self.model1.params)
def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 30))
self.x1 = T.tensor3('x1')
self.x2 = T.tensor3('x2')
self.y = T.vector('y') #0 or 1
self.z = T.vector('z') #-1 or 1
self.model1 = NN_layers.MLPLayer(self.numpy_rng, self.theano_rng, self.x1, INPUT_SHAPE, n_out=OUTPUT_SHAPE[1], batch_size=BATCH_SIZE, W=None, b=None)
self.model2 = NN_layers.MLPLayer(self.numpy_rng, self.theano_rng, self.x2, INPUT_SHAPE, n_out=OUTPUT_SHAPE[1], batch_size=BATCH_SIZE, W=self.model1.W, b=self.model1.b)
self.params = []
self.params.extend(self.model1.params)
def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 30))
self.x1 = T.tensor3('x1')
self.x2 = T.tensor3('x2')
self.y = T.vector('y') #0 or 1
self.z = T.vector('z') #-1 or 1
self.model1 = NN_layers.dotMeanLayer(self.numpy_rng, self.theano_rng, self.x1, INPUT_SHAPE, n_out=OUTPUT_SHAPE[1], W=None)
self.model2 = NN_layers.dotMeanLayer(self.numpy_rng, self.theano_rng, self.x2, INPUT_SHAPE, n_out=OUTPUT_SHAPE[1], W=self.model1.W)
self.params = []
self.params.extend(self.model1.params)
def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 30))
self.x1 = T.tensor3('x1')
self.x2 = T.tensor3('x2')
self.l1 = T.vector('l1')
self.l2 = T.vector('l2')
self.y = T.vector('y') #0 or 1
self.z = T.vector('z') #-1 or 1
self.model1 = NN_layers.quarticWeightsDotMeanLayer(self.numpy_rng, self.theano_rng, self.x1, INPUT_SHAPE,
self.l1, max_length=35, n_out=OUTPUT_SHAPE[1], batch_size=BATCH_SIZE, W=None)
self.model2 = NN_layers.quarticWeightsDotMeanLayer(self.numpy_rng, self.theano_rng, self.x2, INPUT_SHAPE,
self.l2, max_length=35, n_out=OUTPUT_SHAPE[1], batch_size=BATCH_SIZE, W=self.model1.W)
self.params = []
self.params.extend(self.model1.params)
def __init__(self):
self.numpy_rng = numpy.random.RandomState(89677)
self.theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 30))
self.x1 = T.tensor3("x1")
self.x2 = T.tensor3("x2")
self.indices1 = T.matrix("i1")
self.indices2 = T.matrix("i2")
self.l1 = T.vector("l1")
self.l2 = T.vector("l2")
self.y = T.vector("y") # 0 or 1
self.z = T.vector("z") # -1 or 1
self.model1 = NN_layers.exponentialTweetDotMeanLayer(
self.numpy_rng,
self.theano_rng,
self.x1,
INPUT_SHAPE,
self.indices1,
self.l1,
max_length=35,
n_out=OUTPUT_SHAPE[1],
batch_size=BATCH_SIZE,
W=None,
)
self.model2 = NN_layers.exponentialTweetDotMeanLayer(
self.numpy_rng,
self.theano_rng,
self.x2,
INPUT_SHAPE,
self.indices2,
self.l2,
max_length=35,
n_out=OUTPUT_SHAPE[1],
batch_size=BATCH_SIZE,
W=self.model1.W,
)
self.params = []
self.params.extend(self.model1.params)
def model(x):
x = tf.reshape(x, [-1, 784])
return geebs_tanh(layer.fcl(x, [784, 100, 100, 10], "FCL1"))
