x = tf.placeholder(tf.float32,[None,in_units])
keep_prob = tf.placeholder(tf.float32)
hidden1 = tf.nn.relu(tf.matmul(x.W1) + b1)
hidden1_drop = tf.nn.dropout(hidden1,keep_prob)
y = tf.nn.softmax(tf.matmul(hidden1_drop,W2) + b2)
y_ = tf.placeholder(tf.float32,[None,10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),
reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)
http://download.csdn.net/detail/jinbaosite/9799825 再看看别人怎么说的。
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