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我是新來tensorflow並試圖實現類似一個簡單的單層線性網絡https://www.tensorflow.org/get_started/mnist/beginners優化在tensorflow非線性不起作用工作
x = tf.placeholder(tf.float32, [None, IN_SIZE], name="input")
W1 = tf.Variable(tf.zeros([IN_SIZE, OUT_SIZE]), name="Weight1")
b1 = tf.Variable(tf.zeros([OUT_SIZE]), name="bias1")
y = tf.matmul(x, W1) + b1
y_ = tf.placeholder(tf.float32, [None, OUT_SIZE], name="target")
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)
該項目按預期工作和我有沒有問題那。但是,我嘗試添加另一個圖層,但只發現W1,b1,W2
瞭解的都是零矩陣,只有偏差b2
包含非零值。下面是我修改的網絡
x = tf.placeholder(tf.float32, [None, IN_SIZE], name="input")
W1 = tf.Variable(tf.zeros([IN_SIZE, L1_SIZE]), name="Weight1")
b1 = tf.Variable(tf.zeros([L1_SIZE]), name="bias1")
y = tf.matmul(x, W1) + b1
W2 = tf.Variable(tf.zeros([L1_SIZE, OUT_SIZE]), name="Weight2")
b2 = tf.Variable(tf.zeros([OUT_SIZE]), name="bias2")
y = tf.nn.relu(y)
y = tf.matmul(y, W2) + b2
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, OUT_SIZE], name="target")
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)