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def load_vgg(sess, vgg_path):
  
  # load the model and weights
  model = tf.saved_model.loader.load(sess, ['vgg16'], vgg_path)

  # Get Tensors to be returned from graph
  graph = tf.get_default_graph()
  image_input = graph.get_tensor_by_name('image_input:0')
  keep_prob = graph.get_tensor_by_name('keep_prob:0')
  layer3 = graph.get_tensor_by_name('layer3_out:0')

khanhnamle1994

def get_summary_n(squad_list, squad_name, nationality_list):
    summary = []

    for i in nationality_list:
        count = 0
        for j in squad_list:
            
            # for overall rating
            O_temp_rating, _  = get_best_squad_n(formation = j, nationality = i, measurement = 'Overall')
            

khanhnamle1994

mulGate = MultiplyGate()
addGate = AddGate()
activation = Tanh()

class RNNLayer:
    def forward(self, x, prev_s, U, W, V):
        self.mulu = mulGate.forward(U, x)
        self.mulw = mulGate.forward(W, prev_s)
        self.add = addGate.forward(self.mulw, self.mulu)
        self.s = activation.forward(self.add)

khanhnamle1994

class Model:
    def __init__(self, word_dim, hidden_dim=100, bptt_truncate=4):
        self.word_dim = word_dim
        self.hidden_dim = hidden_dim
        self.bptt_truncate = bptt_truncate
        self.U = np.random.uniform(-np.sqrt(1. / word_dim), np.sqrt(1. / word_dim), (hidden_dim, word_dim))
        self.W = np.random.uniform(-np.sqrt(1. / hidden_dim), np.sqrt(1. / hidden_dim), (hidden_dim, hidden_dim))
        self.V = np.random.uniform(-np.sqrt(1. / hidden_dim), np.sqrt(1. / hidden_dim), (word_dim, hidden_dim))

khanhnamle1994

def forward_propagation(self, x):
    # The total number of time steps
    T = len(x)
    layers = []
    prev_s = np.zeros(self.hidden_dim)
    # For each time step...
    for t in range(T):
        layer = RNNLayer()
        input = np.zeros(self.word_dim)
        input[x[t]] = 1

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class Softmax:
    def predict(self, x):
        exp_scores = np.exp(x)
        return exp_scores / np.sum(exp_scores)
    def loss(self, x, y):
        probs = self.predict(x)
        return -np.log(probs[y])
    def diff(self, x, y):
        probs = self.predict(x)
        probs[y] -= 1.0

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def calculate_loss(self, x, y):
    assert len(x) == len(y)
    output = Softmax()
    layers = self.forward_propagation(x)
    loss = 0.0
    for i, layer in enumerate(layers):
        loss += output.loss(layer.mulv, y[i])
    return loss / float(len(y))

def calculate_total_loss(self, X, Y):

khanhnamle1994

def bptt(self, x, y):
    assert len(x) == len(y)
    output = Softmax()
    layers = self.forward_propagation(x)
    dU = np.zeros(self.U.shape)
    dV = np.zeros(self.V.shape)
    dW = np.zeros(self.W.shape)

    T = len(layers)
    prev_s_t = np.zeros(self.hidden_dim)

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def sgd(self, x, y, learning_rate):
    dU, dW, dV = self.bptt(x, y)
    self.U -= learning_rate * dU
    self.V -= learning_rate * dV
    self.W -= learning_rate * dW

khanhnamle1994

class Tanh:
    def forward(self, x):
        return np.tanh(x)
    def backward(self, x, top_diff):
        output = self.forward(x)
        return (1.0 - np.square(output)) * top_diff