title: Training Resnet-50 with CIFAR-100 dataset in TensorFlow, can't get good accuracy
body: <p>I am trying to a resnet-50 model in tensorflow by cifar-100 dataset.I have used builtin resnet_v1_50 to create model in tensorflow with two fully connected layer on it's head.But my validation accuracy stuck at nearly 37%.What is the problem???am I configure wrongly define and configure resnet_v1_50??? my model creation code is given below.</p>
  
  <pre class="lang-py prettyprint-override"><code>import tensorflow as tf
  from tensorflow.contrib.slim.python.slim.nets import resnet_v1
  
  
  X = tf.placeholder(dtype=tf.float32, shape=[None, 32, 32, 3])
  
  Y = tf.placeholder(dtype=tf.float32, shape=[None, 100])
  
  
  net, end_points = resnet_v1.resnet_v1_50(X,global_pool=False,is_training=True)
  
  flattened = tf.contrib.layers.flatten(net)
  
  dense_fc1 = tf.layers.dense(inputs=flattened,units=625, activation=tf.nn.relu,kernel_initializer=tf.contrib.layers.xavier_initializer())
  
  dropout_fc1 = tf.layers.dropout(inputs=dense_fc1,rate=0.5, training=self.training)
  
  logits = tf.layers.dense(inputs=dropout_fc1, units=num_classes,kernel_initializer = tf.contrib.layers.xavier_initializer())
  
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
  
  optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
  </code></pre>