MLP 레이어에 out_layer 추가

[februa22]

추가 완료:

def create_weight_and_bias(n_input, n_output):
    weights = {
        'w1': tf.get_variable('w1', shape=[n_input, n_output], dtype=tf.float32),
        'w2': tf.get_variable('w2', shape=[n_output, n_output], dtype=tf.float32),
    }
    biases = {
        'b1': tf.get_variable('b1', shape=[n_output], dtype=tf.float32, initializer=tf.zeros_initializer()),
        'b2': tf.get_variable('b2', shape=[n_output], dtype=tf.float32, initializer=tf.zeros_initializer()),
    }
    return weights, biases

def multilayer_perceptron(x, weights, biases, dropout):
    layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    out_layer = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])
    if dropout > 0.0:
        keep_prob = 1.0 - dropout
        out_layer = tf.nn.dropout(out_layer, keep_prob)
    return out_layer