def __call__(self, shape, name=None): flat_shape = (shape[0], np.prod(shape[1:])) a = np_rng.normal(0.0, 1.0, flat_shape) u, _, v = np.linalg.svd(a, full_matrices=False) q = u if u.shape == flat_shape else v # pick the one with the correct shape q = q.reshape(shape) return sharedX(self.scale * q[:shape[0], :shape[1]], name=name)
def __call__(self, shape, name=None): flat_shape = (shape[0], np.prod(shape[1:])) a = np_rng.normal(0.0, 1.0, flat_shape) u, _, v = np.linalg.svd(a, full_matrices=False) q = u if u.shape == flat_shape else v # pick the one with the correct shape q = q.reshape(shape) return sharedX(self.scale * q[:shape[0], :shape[1]], name=name)
def __call__(self, shape): if len(shape) == 2: scale = np.sqrt(2. / shape[0]) elif len(shape) == 4: scale = np.sqrt(2. / np.prod(shape[1:])) else: raise NotImplementedError return sharedX(np_rng.normal(size=shape, scale=scale))
def __call__(self, shape): if len(shape) == 2: scale = np.sqrt(2./shape[0]) elif len(shape) == 4: scale = np.sqrt(2./np.prod(shape[1:])) else: raise NotImplementedError return sharedX(np_rng.normal(size=shape, scale=scale))
def init(self): naxes = len(self.out_shape) if naxes == 2 or naxes == 4: dim = self.out_shape[1] elif naxes == 3: dim = self.out_shape[-1] else: raise NotImplementedError self.g = inits.Constant(c=1.)(dim) self.b = inits.Constant(c=0.)(dim) self.u = inits.Constant(c=0.)(dim) self.s = inits.Constant(c=0.)(dim) self.n = sharedX(0.) self.params = [self.g, self.b] self.other_params = [self.u, self.s, self.n]
def init(self): naxes = len(self.out_shape) if naxes == 2 or naxes == 4: dim = self.out_shape[1] elif naxes == 3: dim = self.out_shape[-1] else: raise NotImplementedError self.g = inits.Constant(c=1.)(dim) self.b = inits.Constant(c=0.)(dim) self.u = inits.Constant(c=0.)(dim) self.s = inits.Constant(c=0.)(dim) self.n = sharedX(0.) self.params = [self.g, self.b] self.other_params = [self.u, self.s, self.n]
def __call__(self, vocab, name=None): t = time() w2v_vocab = joblib.load(os.path.join(self.data_dir, '3m_w2v_gn_vocab.jl')) w2v_embed = joblib.load(os.path.join(self.data_dir, '3m_w2v_gn.jl')) mapping = {} for i, w in enumerate(w2v_vocab): w = w.lower() if w in mapping: mapping[w].append(i) else: mapping[w] = [i] widxs = [] w2vidxs = [] for i, w in enumerate(vocab): w = w.replace('`', "'") if w in mapping: w2vi = min(mapping[w]) w2vidxs.append(w2vi) widxs.append(i) w = np.zeros((len(vocab), w2v_embed.shape[1])) w[widxs, :] = w2v_embed[w2vidxs, :]/2. return sharedX(w, name=name)
def __call__(self, vocab, name=None): t = time() w2v_vocab = joblib.load( os.path.join(self.data_dir, '3m_w2v_gn_vocab.jl')) w2v_embed = joblib.load(os.path.join(self.data_dir, '3m_w2v_gn.jl')) mapping = {} for i, w in enumerate(w2v_vocab): w = w.lower() if w in mapping: mapping[w].append(i) else: mapping[w] = [i] widxs = [] w2vidxs = [] for i, w in enumerate(vocab): w = w.replace('`', "'") if w in mapping: w2vi = min(mapping[w]) w2vidxs.append(w2vi) widxs.append(i) w = np.zeros((len(vocab), w2v_embed.shape[1])) w[widxs, :] = w2v_embed[w2vidxs, :] / 2. return sharedX(w, name=name)
def __call__(self, shape): return sharedX(np.identity(shape[0]) * self.scale)
def __call__(self, shape): return sharedX(np.ones(shape) * self.c)
def __call__(self, shape, name=None): r = np_rng.normal(loc=0, scale=0.01, size=shape) r = r/np.sqrt(np.sum(r**2))*np.sqrt(shape[1]) return sharedX(r, name=name)
def __call__(self, shape, name=None): return sharedX(np_rng.normal(loc=self.loc, scale=self.scale, size=shape), name=name)
def __call__(self, shape): return sharedX(np.ones(shape) * self.c)
def __call__(self, shape, name=None): r = np_rng.normal(loc=0, scale=0.01, size=shape) r = r / np.sqrt(np.sum(r**2)) * np.sqrt(shape[1]) return sharedX(r, name=name)
def __call__(self, shape, name=None): return sharedX(np_rng.normal(loc=self.loc, scale=self.scale, size=shape), name=name)
def __call__(self, shape): return sharedX( np_rng.uniform(low=-self.scale, high=self.scale, size=shape))
#get the cost functions from both the real, generated, and discriminator functions d_cost_real = bce(p_real, T.ones(p_real.shape)).mean() d_cost_gen = bce(p_gen, T.zeros(p_gen.shape)).mean() g_cost_d = bce(p_gen, T.ones(p_gen.shape)).mean() #get the cost functions from the discriminator function d_cost = d_cost_real + d_cost_gen g_cost = g_cost_d #create the grand cost function cost = [g_cost, d_cost, d_cost_real, d_cost_gen] #update the weights using the 'adam' method https://arxiv.org/pdf/1412.6980 lr = 0.001 lrt = sharedX(lr) d_updater = updates.Adam(lr=lrt) g_updater = updates.Adam(lr=lrt) d_updates = d_updater(d_params, d_cost) g_updates = g_updater(g_params, g_cost) updates = d_updates + g_updates #get the final variables for both networks, cost, and score (to keep track of things) _train_g = theano.function([X, Z], cost, updates=g_updates) _train_d = theano.function([X, Z], cost, updates=d_updates) _train_both = theano.function([X, Z], cost, updates=updates) _gen = theano.function([Z], gen) _score = theano.function([X], p_real) _cost = theano.function([X, Z], cost)
def __call__(self, shape): return sharedX(np.identity(shape[0]) * self.scale)
#get the cost functions from both the real, generated, and discriminator functions d_cost_real = bce(p_real, T.ones(p_real.shape)).mean() d_cost_gen = bce(p_gen, T.zeros(p_gen.shape)).mean() g_cost_d = bce(p_gen, T.ones(p_gen.shape)).mean() #get the cost functions from the discriminator function d_cost = d_cost_real + d_cost_gen g_cost = g_cost_d #create the grand cost function cost = [g_cost, d_cost, d_cost_real, d_cost_gen] #update the weights using the 'adam' method https://arxiv.org/pdf/1412.6980 lr = 0.001 lrt = sharedX(lr) d_updater = updates.Adam(lr=lrt) g_updater = updates.Adam(lr=lrt) d_updates = d_updater(d_params, d_cost) g_updates = g_updater(g_params, g_cost) updates = d_updates + g_updates #get the final variables for both networks, cost, and score (to keep track of things) _train_g = theano.function([X, Z], cost, updates=g_updates) _train_d = theano.function([X, Z], cost, updates=d_updates) _train_both = theano.function([X, Z], cost, updates=updates) _gen = theano.function([Z], gen) _score = theano.function([X], p_real) _cost = theano.function([X, Z], cost)
def __call__(self, shape): return sharedX(np_rng.uniform(low=-self.scale, high=self.scale, size=shape))