def __init__(self, layer_sizes): """Model for encoding user queries. ...for layer_size in layer_sizes[-1:]: self.dense_layers.add(tf.keras.layers.Dense(layer_size)) ...def __init__(self, layer_sizes): """Model for encoding movies. ...for layer_size in layer_sizes[-1:]: self.dense_layers.add(tf.keras.layers.Dense(layer_size)) ...__init__() self.query_model = QueryModel(layer_sizes) self.candidate_model = CandidateModel(layer_sizes
self.conv5 = SpatioTemporalResLayer(256, 512, 3, layer_sizes[3], downsample=True) self.pool...(512, 3, layer_sizes[3], downsample=True) self.pool = layers.GlobalAveragePooling3D()...[0]) # 带降采样的Res(2+1)D卷积 self.conv3 = SpatioTemporalResLayer(64, 128, 3, layer_sizes[1...[0]) self.conv3 = SpatioTemporalResLayer(64, 128, 3, layer_sizes[1], downsample=True)...self.conv4 = SpatioTemporalResLayer(128, 256, 3, layer_sizes[2], downsample=True) self.conv5
pred_probs) * (1 - targets) return -np.mean(np.log(label_probabilities)) def build_standard_net(layer_sizes...layer_sizes = layer_sizes + [1] parser = WeightsParser() for i, shape in enumerate(zip(layer_sizes...[:-1], layer_sizes[1:])): parser.add_weights(('weights', i), shape) parser.add_weights...1, shape[1])) def predictions(W_vect, X): cur_units = X for layer in range(len(layer_sizes...biases', layer)) cur_units = np.dot(cur_units, cur_W) + cur_B if layer < len(layer_sizes
def layer_sizes(X, Y): """ 定义神经网络结构 :param X: 形状的输入数据集(输入大小，示例数量) :param Y: 形状标签(输出尺寸...""" np.random.seed(3) n_x = layer_sizes(X, Y)[0] n_y = layer_sizes(X, Y)[2] parameters...加载数据 X, Y = load_planar_dataset() # 获取数据的形状 shape_X = X.shape shape_Y = Y.shape m = shape_X[1] def layer_sizes...""" np.random.seed(3) n_x = layer_sizes(X, Y)[0] n_y = layer_sizes(X, Y)[2] parameters
__init__() layer_sizes = [x_dim] + [h_dim] * nb_layers + [out_dim] self.gcn_layers...= [ GCNLayer(in_dim, out_dim, bias) for in_dim, out_dim in zip(layer_sizes...[:-1], layer_sizes[1:]) ] self.dropout = nn.Dropout(p=dropout) def __call__(
)-1) y_space = (top-bottom) / float(max(layer_sizes)) p = 0.025 # 中间节点 for i,n in enumerate...(layer_sizes): top_on_layer = y_space*(n-1)/2.0 + (top+bottom)/2.0 layer = lst_layers[i...] color = "green" if i in [0, len(layer_sizes)-1] else "blue" color = "red" if (layer['...fontsize=10, color=color, s="Σ"+str(layer['in'])+"[X*w]+b") out = " Y" if i == len(layer_sizes...[:-1], layer_sizes[1:])): layer = lst_layers[i+1] color = "green" if i == len(layer_sizes
# GRADED FUNCTION: layer_sizes def layer_sizes(X, Y): """ Arguments: X -- input dataset...HERE ### return (n_x, n_h, n_y) X_assess, Y_assess = layer_sizes_test_case() (n_x, n_h, n_y) = layer_sizes...They can then be used to predict. """ np.random.seed(3) n_x = layer_sizes(X, Y)[0]...n_y = layer_sizes(X, Y)[2] # Initialize parameters, then retrieve W1, b1, W2, b2.
注意，layer_sizes列表的长度必须等于num_samples的长度，因为len(num_samples)定义了GraphSAGE编码器的跳数（层数）。...layer_sizes = [50, 50] graphsage = GraphSAGE( layer_sizes=layer_sizes, generator=generator, bias=
定义神经网络结构 在构建之前，我们要先把神经网络的结构给定义好： n_x: 输入层的数量 n_h: 隐藏层的数量（这里设置为4） n_y: 输出层的数量 def layer_sizes(X , Y):...() (n_x,n_h,n_y) = layer_sizes(X_asses,Y_asses) print("输入层的节点数量为: n_x = " + str(n_x)) print("隐藏层的节点数量为...: n_h = " + str(n_h)) print("输出层的节点数量为: n_y = " + str(n_y)) 运行结果如下： =========================测试layer_sizes...""" np.random.seed(3) #指定随机种子 n_x = layer_sizes(X, Y)[0] n_y = layer_sizes(X, Y)[2]...""" np.random.seed(3) #指定随机种子 n_x = layer_sizes(X, Y)[0] n_y = layer_sizes(X, Y)[2]
模型训练 def my_model(X, Y, n_h, num_iterations=10000, print_cost=False): np.random.seed(3) n_x = layer_sizes...(X, Y)[0] n_y = layer_sizes(X, Y)[2] parameters = initialize_parameters(n_x, n_h, n_y) w1 =
编写辅助函数，计算步骤1-3 将它们合并到 nn_model（）的函数中 学习正确的参数，对新数据进行预测 4.1 定义神经网络结构 定义每层的节点个数 # GRADED FUNCTION: layer_sizes...def layer_sizes(X, Y): """ Arguments: X -- input dataset of shape (input size, number of...They can then be used to predict. """ np.random.seed(3) n_x = layer_sizes(X, Y)[0]...n_y = layer_sizes(X, Y)[2] # Initialize parameters, then retrieve W1, b1, W2, b2.
. # GRADED FUNCTION: layer_sizes def layer_sizes(X, Y): """ Arguments: X -- input dataset...HERE ### return (n_x, n_h, n_y) X_assess, Y_assess = layer_sizes_test_case() (n_x, n_h, n_y) = layer_sizes...They can then be used to predict. """ np.random.seed(3) n_x = layer_sizes(X, Y)[0]...n_y = layer_sizes(X, Y)[2] # Initialize parameters, then retrieve W1, b1, W2, b2.
1240] 上面是正向传播，下面是反向传播，并通过计算导数，进行梯度下降优化 反向传播的每层隐藏层input output [1240][1240][1240] #代码 1 - 定义神经网络结构 def layer_sizes...They can then be used to predict. """ np.random.seed(3) n_x = layer_sizes(X, Y)[0] n_y...= layer_sizes(X, Y)[2] # Initialize parameters, then retrieve W1, b1, W2, b2.
暂时还没搞懂，看上去是个节点生成器之类的 train_gen = generator.flow(train_subjects.index, train_targets) gcn = GCN( layer_sizes
画一个多层感知机 1import matplotlib.pyplot as plt 2import networkx as nx 3left, right, bottom, top, layer_sizes...)) 9h_spacing = (right - left)/float(len(layer_sizes) - 1) 10node_count = 0 11for i, v in enumerate(...layer_sizes): 12 layer_top = v_spacing*(v-1)/2. + (top + bottom)/2. 13 for j in range(v): 14...[:-1], layer_sizes[1:])): 18 for i in range(left_nodes): 19 for j in range(right_nodes): 20...G.add_edge(i+sum(layer_sizes[:x]), j+sum(layer_sizes[:x+1])) 21 22pos=nx.get_node_attributes
—— # coding:utf8 import numpy as np # 定义激活函数 def sigmoid(x): return 1.0/(1+np.exp(-x)) #定义网络 def layer_sizes
s/1eDwOxweRDPurI2fF51EALQ class FNN(Model): def __init__(self, field_sizes=None, embed_size=10, layer_sizes...embed_size], 'xavier', dtype)) node_in = num_inputs * embed_size for i in range(len(layer_sizes...)): init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))...init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype)) node_in = layer_sizes[i]...None: self.loss += embed_l2 * tf.nn.l2_loss(xw) for i in range(len(layer_sizes
pretrained: imagenet embedder: MLP: layer_sizes
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