Adding dropout (tf.nn.dropout) results in Nan - tensorflow

Question

Being a beginner to tensorflow and CNN I'm working on emotion recognition to understand these.
The following code works when dropout layer is removed, however results in Nan when added. I've googled around and came across solutions such as reducing learning rate etc. None has worked for me.
The net :
def cnn(self, data):
conv = tf.nn.conv2d(data, self.w_1, [1, 1, 1, 1], padding='SAME')
hidden = tf.nn.relu(conv + self.b_1)
pool = tf.nn.max_pool(hidden, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
norm = tf.nn.lrn(pool, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
conv = tf.nn.conv2d(norm, self.w_2, [1, 1, 1, 1], padding='SAME')
hidden = tf.nn.relu(conv + self.b_2)
pool = tf.nn.max_pool(hidden, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
norm = tf.nn.lrn(pool, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
list_shape = norm.get_shape().as_list()
reshape = tf.reshape(pool, [list_shape[0], list_shape[1] * list_shape[2] * list_shape[3]])
hidden = tf.nn.relu(tf.matmul(reshape, self.w_3) + self.b_3)
hidden = tf.nn.relu(tf.matmul(hidden, self.w_4) + self.b_4)
dropout = tf.nn.dropout(hidden, self.dropout_prob)
return tf.matmul(dropout, self.w_5) + self.b_5
The model:
self.tf_x = tf.placeholder(tf.float32, shape=(self.batch_size, self.image_size, self.image_size, 1))
self.tf_y = tf.placeholder(tf.float32, shape=(self.batch_size, self.num_labels))
self.dropout_prob = tf.placeholder(tf.float32)
self.w_1 = tf.Variable(tf.truncated_normal([5, 5, 1, 64], stddev=0.1))
self.b_1 = tf.Variable(tf.zeros([64]))
self.w_2 = tf.Variable(tf.truncated_normal([9, 9, 64, 128], stddev=0.04))
self.b_2 = tf.Variable(tf.constant(1.0, shape=[128]))
self.w_3 = tf.Variable(tf.truncated_normal([self.image_size//4 * self.image_size//4 * 128, 392], stddev=0.1))
self.b_3 = tf.Variable(tf.constant(1.0, shape=(392,)))
self.w_4 = tf.Variable(tf.truncated_normal([392, 196], stddev=0.1))
self.b_4 = tf.Variable(tf.constant(1.0, shape=(196,)))
self.w_5 = tf.Variable(tf.truncated_normal([196, self.num_labels], stddev=0.04))
self.b_5 = tf.Variable(tf.constant(1.0, shape=[self.num_labels]))
self.logits = self.cnn(self.tf_x)
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.tf_y, logits=self.logits))
self.optimizer = tf.train.AdamOptimizer(1e-6).minimize(self.loss)
self.train_pred = tf.nn.softmax(self.logits)
tf.summary.histogram('weights_1', self.w_1)
tf.summary.histogram('weights_2', self.w_2)
tf.summary.histogram('weights_3', self.w_3)
tf.summary.histogram('weights_4', self.w_4)
tf.summary.scalar('loss', self.loss)
self.merged = tf.summary.merge_all()
The error:
Traceback (most recent call last):
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\main.py", line 75, in <module>
main()
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\main.py", line 64, in main
emotion_cnn.train_test_validate()
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\Emotion.py", line 127, in train_test_validate
_,summary, l1, predictions1 = self.session.run([self.optimizer, self.merged, self.loss, self.train_pred], feed_dict=feed_dict1)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\client\session.py", line 767, in run
run_metadata_ptr)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\client\session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\client\session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\client\session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Nan in summary histogram for: weights_1
[[Node: weights_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](weights_1/tag, Variable/read/_81)]]
Caused by op 'weights_1', defined at:
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\main.py", line 75, in <module>
main()
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\main.py", line 64, in main
emotion_cnn.train_test_validate()
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\Emotion.py", line 104, in train_test_validate
self.model()
File "C:\Users\joyte\Documents\GitHub\Emotion-recognizer\Emotion.py", line 82, in model
tf.summary.histogram('weights_1', self.w_1)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\summary\summary.py", line 203, in histogram
tag=scope.rstrip('/'), values=values, name=scope)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 139, in _histogram_summary
name=name)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\joyte\Anaconda3\envs\ML\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Nan in summary histogram for: weights_1
[[Node: weights_1 = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](weights_1/tag, Variable/read/_81)]]
E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_gpu_executor.cc:637] Deallocating stream with pending work