我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用keras.initializations.glorot_uniform()。
def prep_embed(self, full_i2w_dict, ind2word, w2v_dim): ''' Prepare embedding vector for each word in full_dict Words which are in word2vec vocab are replaced by respective wordvector OOV words i.e words that are not in word2vec are replaced by random weight(rand_weight) ''' embed_weight=np.zeros((len(full_i2w_dict),w2v_dim)) embed_dict={} for k,v in full_i2w_dict.items(): if k in ind2word: model_weight=np.array(self.model[v]) embed_weight[k]=model_weight embed_dict[k]=model_weight else: rand_weight=np.array(glorot_uniform((w2v_dim,)).eval()) embed_weight[k]=rand_weight embed_dict[k]=rand_weight return embed_weight, embed_dict
def create_conv_model(self): # This is the place where neural network model initialized init = 'glorot_uniform' self.state_in = Input(self.state_dim) self.l1 = Convolution2D(32, 8, 8, activation='elu', init=init, subsample=(4, 4), border_mode='same')( self.state_in) self.l2 = Convolution2D(64, 4, 4, activation='elu', init=init, subsample=(2, 2), border_mode='same')( self.l1) # self.l3 = Convolution2D(64, 3, 3, activation='relu', init=init, subsample=(1, 1), border_mode='same')( # self.l2) self.l3 = self.l2 self.h = Flatten()(self.l3) self.hidden = Dense(256, init=init, activation='elu')(self.h) self.value = Dense(1, init=init)(self.hidden) self.policy = Dense(self.action_dim, init=init, activation='softmax')(self.hidden) self.q_values = self.entropy_coef * (Theano.log(self.policy + 1e-18) - Theano.tile(Theano.sum(Theano.log(self.policy + 1e-18) * self.policy, axis=[1], keepdims=True), (1, self.action_dim))) self.q_values = self.q_values + Theano.tile(self.value, (1, self.action_dim)) self.model = Model(self.state_in, output=[self.policy, self.value])
def create_fc_model(self): # This is the place where neural network model initialized init = 'glorot_uniform' self.state_in = Input(self.state_dim) self.hidden = Dense(256, init=init, activation='elu')(self.state_in) self.value = Dense(1)(self.hidden) self.policy = Dense(self.action_dim, init=init, activation='softmax')(self.hidden) self.q_values = self.entropy_coef * (Theano.log(self.policy + 1e-18) - Theano.tile(Theano.sum(Theano.log(self.policy + 1e-18) * self.policy, axis=[1], keepdims=True), (1, self.action_dim))) # print (type(Theano.sum(Theano.log(self.policy + 1e-18) * self.policy, # axis=[1], keepdims=True))) # print(Theano.function([self.state_in], [Theano.sum(Theano.log(self.policy + 1e-18) * self.policy, # axis=[1], keepdims=True)])([np.zeros((32,) + self.state_dim)])[0].shape) # 1/0 self.q_values = self.q_values + Theano.tile(self.value, (1, self.action_dim)) self.model = Model(self.state_in, output=[self.policy, self.value])
def test_glorot_uniform(tensor_shape): scale = np.sqrt(6. / (SHAPE[0] + SHAPE[1])) _runner(initializations.glorot_uniform, tensor_shape, target_mean=0., target_max=scale, target_min=-scale)
def unitary_ASB2016_init(shape, name=None): assert shape[0]==shape[1] N=shape[1] theta = initializations.uniform((3,N),scale=np.pi,name='{}_theta'.format(name)) reflection = initializations.glorot_uniform((2,2*N),name='{}_reflection'.format(name)) idxperm = np.random.permutation(N) idxpermaug = np.concatenate((idxperm,N+idxperm)) Iaug=augLeft(np.concatenate((np.eye(N),np.zeros((N,N))),axis=0),module=np).astype(np.float32) Uaug=times_unitary_ASB2016(Iaug,N,[theta,reflection,idxpermaug]) return Uaug,theta,reflection,idxpermaug
def make_hash_embeddings(igor, vocab): assert os.path.exists(igor.target_glove), "You need to specify a real file" fileiter = open(igor.target_glove).readlines() hash_vocab = Vocabulary() hash_vocab.use_mask = True hash_vocab.add(hash_vocab.mask_symbol) hash_vocab.add(hash_vocab.unk_symbol) word2hash = {} for word, v_id in vocab.items(): ids = hash_vocab.add_many(hash_word(word)) word2hash[v_id] = ids embeddings = np.zeros((len(hash_vocab), igor.embedding_size)) remaining_vocab = set(vocab.keys()) remaining_hashes = set(hash_vocab.values()) for line in tqdm(fileiter): line = line.replace("\n","").split(" ") word, nums = line[0], [float(x.strip()) for x in line[1:]] word_hash = hash_word(word) if word in remaining_vocab: hash_ids = word2hash[vocab[word]] remaining_vocab.remove(word) remaining_hashes.difference_update(hash_ids) embeddings[hash_ids] += np.array(nums) / len(hash_ids) print("{} words were not seen. {} hashes were not seen".format(len(remaining_vocab), len(remaining_hashes))) for hash_id in remaining_hashes: embeddings[hash_id] = np.asarray(glorot_uniform((igor.embedding_size,)).eval()) glove_name = igor.target_glove[igor.target_glove.find("glove"):].replace("/","") hash_vocab.save('hash_embedding_{}.vocab'.format(glove_name)) with open(path.join(igor.save_dir, "hash_embedding_{}.npy".format(glove_name)), "wb") as fp: np.save(fp, embeddings) with open(path.join(igor.save_dir, "word2hash.json".format(glove_name)), "w") as fp: json.dump(word2hash, fp)
def from_vocab(igor, vocab): print("using vocab and glove file to generate embedding matrix") remaining_vocab = set(vocab.keys()) embeddings = np.zeros((len(vocab), igor.embedding_size)) print("{} words to convert".format(len(remaining_vocab))) if igor.save_dir[-1] != "/": igor.save_dir += "/" if not path.exists(igor.save_dir): makedirs(igor.save_dir) if igor.from_url: assert hasattr(glove_urls, igor.target_glove), "You need to specify one of the glove variables" url = urlopen(getattr(glove_urls, igor.target_glove)) fileiter = ZipFile(StringIO(url.read())).open(file).readlines() else: assert os.path.exists(igor.target_glove), "You need to specify a real file" fileiter = open(igor.target_glove).readlines() count=0 for line in tqdm(fileiter): line = line.replace("\n","").split(" ") try: word, nums = line[0], [float(x.strip()) for x in line[1:]] if word in remaining_vocab: embeddings[vocab[word]] = np.array(nums) remaining_vocab.remove(word) except Exception as e: print("{} broke. exception: {}. line: {}.".format(word, e, x)) count+=1 print("{} words were not in glove; saving to oov.txt".format(len(remaining_vocab))) with open(path.join(igor.save_dir, "oov.txt"), "w") as fp: fp.write("\n".join(remaining_vocab)) for word in tqdm(remaining_vocab): embeddings[vocab[word]] = np.asarray(glorot_uniform((igor.embedding_size,)).eval()) vocab.save('embedding.vocab') with open(path.join(igor.save_dir, "embedding.npy"), "wb") as fp: np.save(fp, embeddings)
