生成初始化为0的张量的初化器。1、__call____call__( shape, dtype=tf.dtypes.float32)返回初始化器指定...
错误总结 bias = tf.get_variable("bias", shape=[out_channels], initializer=tf.zeros_initializer()) 中tf.zeros_initializer...一定要加括号,不然会报错,因为tf.zeros_initializer是个类,加了括号表示创建了这个类的一个对象。
kernel_size=[3,3], padding="same", activation=tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer...kernel_size=[3,3], padding="same", activation=tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer...kernel_size=[3,3], padding="same", activation=tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer...inputs=pool5_flat, units=4096, activation=tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer...inputs=dropout1, units=4096, activation=tf.nn.relu, use_bias=True, trainable=True, bias_initializer=tf.zeros_initializer
shape=[input_shape[-1], self.units], initializer=tf.zeros_initializer...shape=[self.units], initializer=tf.zeros_initializer...dtype=tf.int32, initializer=tf.zeros_initializer...dtype=tf.int32, initializer=tf.zeros_initializer
axis = list(range(len(x_shape) - 1)) beta = _get_variable('beta', params_shape, initializer=tf.zeros_initializer...initializer=tf.ones_initializer()) moving_mean = _get_variable('moving_mean', params_shape, initializer=tf.zeros_initializer
shape= [COVN_1_CHANNELS], initializer = tf.zeros_initializer...kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer...kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer...kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer...kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer
tf.keras.layers.Dense( units=1, activation=None, kernel_initializer=tf.zeros_initializer...(), bias_initializer=tf.zeros_initializer() ) def call(self, input): # 重载 call
self.w = self.add_variable(name='w', 8 shape=[input_shape[-1], self.units], initializer=tf.zeros_initializer...()) 9 self.b = self.add_variable(name='b', 10 shape=[self.units], initializer=tf.zeros_initializer...__init__() 4 self.total = self.add_weight(name='total', dtype=tf.int32, initializer=tf.zeros_initializer...()) 5 self.count = self.add_weight(name='count', dtype=tf.int32, initializer=tf.zeros_initializer
tf.zeros_initializer() 和 tf.ones_initializer() 现在返回一个 callable,其必须用 initializer 参数调用,在你的代码中用 tf.zeros_initializer
由它衍生出两个初始化方法: tf.zeros_initializer:可以简写为tf.Zeros。 tf.ones_initializer:可以简写为tf.Ones。...bias_initializer=tf.Constant(0)) bias_initializer = tf.constant_initializer(0) # 方法2 bias_initializer = tf.zeros_initializer
initializer=tf.random_normal_initializer()) b = tf.get_variable(name="bias", shape=(1, 10), initializer=tf.zeros_initializer
tf.zeros_initializer()和tf.ones_initializer()现在返回一个必须用initializer参数调用的可调用值,在代码中用tf.zeros_initializer()...替换tf.zeros_initializer。
--tf.zeros_initializer() 和tf.ones_initializer() 现在返回一个callable,其必须用initializer 参数调用,在你的代码中用tf.zeros_initializer
fc_initializer()) bias = create_var("bias", [num_outputs,], tf.zeros_initializer...scope): beta = create_var("beta", [num_inputs,], initializer=tf.zeros_initializer...moving_mean = create_var("moving_mean", [num_inputs,], initializer=tf.zeros_initializer
channels_last', dilation_rate=1, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer
tf.keras.layers.Dense( units=1, activation=None, kernel_initializer=tf.zeros_initializer...(), bias_initializer=tf.zeros_initializer() ) def call(self, input):
tf.contrib.layers.xavier_initializer(seed = 1)) b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer...tf.contrib.layers.xavier_initializer(seed = 1)) b2 = tf.get_variable("b2", [12,1], initializer = tf.zeros_initializer...tf.contrib.layers.xavier_initializer(seed = 1)) b3 = tf.get_variable("b3", [6,1], initializer = tf.zeros_initializer
activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer...activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer...activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer...activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer
= tf.contrib.layers.xavier_initializer(seed = 1)) b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer...initializer=tf.contrib.layers.xavier_initializer(seed=1)) b1 = tf.get_variable('b1',[25,1],initializer=tf.zeros_initializer...initializer=tf.contrib.layers.xavier_initializer(seed=1)) b2 = tf.get_variable('b2',[12,1],initializer=tf.zeros_initializer...initializer=tf.contrib.layers.xavier_initializer(seed=1)) b3 = tf.get_variable('b3',[6,1],initializer=tf.zeros_initializer
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