self.inplace = inplace def get_params(self, img, scale, ratio): img = np.array(img) img_h, img_w, img_c = img.shape s = random.uniform(*scale) r = random.uniform(*ratio) s = s * img_h * img_w w = int(math.sqrt(s
隐藏单元的个数是超参数 # num_outputs: q def get_params(): def _one(shape): param = torch.zeros(shape, device=device, dtype=torch.float32) nn.init.normal_(param, 0, 0.01) return torch.nn.Parameter(param) # 权重参数随机初始化,偏置参数置0 # 隐藏层参数 W_xh...
net = nn.Sequential([nn.Dense(8), nn.relu, nn.Dense(1)]) X = jax.random.uniform(d2l.get_key(), (2, 4)) params = net.init(d2l.get_key(), X) net.apply(params, X).shape (2, 1) net = tf.keras.models.Sequential([ tf.keras.layers.Flatten(), tf.keras.layers.Dense(4, ac...
[2, 1027]) #初始化模型参数 num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size #vocab_size=1027 # num_inputs: d # num_hiddens: h, 隐藏单元的个数是超参数 # num_outputs: q def get_params(): def _one(shape): param = torch.zeros(shape, device=device, dtype=...
getparams().framerate # 获取音频时间长度 t = int(a / f) print('总时长为 %d s' % t) # 读取语音 sound = AudioSegment.from_wav(wave_path) for start_time in range(0, t, crop_len): save_path = os.path.join(path, os.path.basename(wave_path)[:-4], str(uuid.uuid1()) + '...
crop时,其中心点坐标和宽高是由get_params方法得到的,首先在scale限定的数值范围内随机生成一个数,用这个数乘以输入图像的面积作为crop后图像的面积,然后在ratio限定的数值范围内随机生成一个数,表示宽高比,根据这两个值就可以得到crop图像的宽高。crop图像的中心点坐标,是类RandomCrop类一样是随机生成的。
train_and_predict_rnn(gru, get_params, init_gru_state, num_hiddens, vocab_size, device, corpus_indices, idx_to_char, char_to_idx, False, num_epochs, num_steps, lr, clipping_theta, batch_size, pred_period, pred_len, prefixes) 输出: 简洁实现 代码语言:javascript 代码运行次数:0 运行 ...
self.center = center@staticmethoddefget_params(degrees):"""Get parameters for ``rotate`` for a random rotation. Returns: sequence: params to be passed to ``rotate`` for random rotation. """angle = np.random.uniform(degrees[0], degrees[1])returnangledef__call__(self, img):""" ...
defaccumulate_params(self): returnget_model_parameters_number(self) defaccumulate_flops(self): ifis_supported_instance(self): returnself.__flops__/model.__batch_counter__ else: sum=0 forminself.children(): sum+=m.accumulate_flops()
length = 100 #TODO: get shape, flatten, multiply... self.policy = AEPolicy(length) self.policy_optim = torch.optim.Adam(self.policy.parameters(), lr=pseudo_lr) super(MetaBackProp, self).__init__(params, pseudo_defaults) def step(self, closure=None): ...