我正在尝试使用tensorflow来实现神经网络。我希望我的模型使用默认参数或用户传递的任何重量尺寸列表进行初始化。但是我的代码生成的计算图似乎不正确。如何通过tensorflow中的用户定义参数初始化NN？
def setUp(self):
self.hidden_layer_ = len(self.hidden_layer_sizes)
self.weights = []
self.biases = []
size_list = [self.input_size]
size_list.extend(list(self.hidden_layer_sizes))
i = 0
while i + 1 < len(size_list):
prev_size = size_list[i]
size = size_list[i+1]
w = tf.Variable(tf.truncated_normal([prev_size, size], stddev=0.1))
b = tf.Variable(tf.zeros([size]))
self.weights.append(w)
self.biases.append(b)
i += 1
self.w_out = tf.Variable(tf.truncated_normal([size_list[-1], self.output_size], stddev=0.1))
self.b_out = tf.Variable(tf.zeros([self.output_size]))
self.input_x = tf.placeholder(tf.float32, [None, self.input_size], name='input')
self.input_y = tf.placeholder(tf.float32, [None, self.output_size], name='label')
self.keep_prob_plh = tf.placeholder(tf.float32)
self.layers = [self.input_x]
for w, b in zip(self.weights, self.biases):
hidden = ACT_FUN_DICT[self.activation](tf.matmul(self.layers[-1], w) + b)
hidden_drop = tf.nn.dropout(hidden, self.keep_prob_plh)
self.layers.append(hidden_drop)
with tf.variable_scope('output'):
self.output_layer = tf.nn.sigmoid(tf.matmul(self.layers[-1], self.w_out) + self.b_out)
self.cost_func = tf.reduce_mean(tf.reduce_sum(tf.pow((self.input_y - self.output_layer), 2)))
self.cost_summary = tf.summary.scalar('Cost', self.cost_func)
self.optimizer = SOLVER_DICT[self.solver](self.learning_rate).minimize(self.cost_func)
root_logdir = './tf_logs'
now = datetime.utcnow().strftime('%Y%m%d%H%M%S')
log_dir = "{}/run-{}/".format(root_logdir, now)
self.file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())
self.sess.run(tf.global_variables_initializer())
我使用类来实现NN，权重的大小存储在成员self.hidden_layer_sizes中。
class MLPClassifier(BaseEstimator, TransformerMixin):
def __init__(self, hidden_layer_sizes=(100,), activation='relu', solver='sgd', alpha=0.0001,
learning_rate=0.001, max_iter=200, random_state=42, keep_prob=0.75, logged = True):
self.hidden_layer_sizes = hidden_layer_sizes
self.activation = activation
self.solver = solver
self.alpha = alpha
self.learning_rate = learning_rate
self.max_iter = max_iter
self.random_state = random_state
self.keep_prob = keep_prob
self.fitted = False
self.logged = True
self.sess = tf.Session()
self.sess.as_default()
这就是我的全部代码，这就是我的compute graph：
