Tensorflow: cost not changing - tensorflow

Question

I build up a CNN in the following structure. The inputs are 32 * 32 * 3 pictures and a one-hot label of 10 categories.
inputs = tf.placeholder(tf.float32, [None, 32, 32, 3], name = "input")
targets = tf.placeholder(tf.float32, [None, 10], name = "targets")
layer_1_filter = tf.layers.conv2d(inputs = inputs,
filters = 64,
kernel_size = (2, 2),
strides = (1, 1),
padding = "same",
activation= tf.nn.relu)
layer_2_pooling = tf.layers.max_pooling2d(inputs = layer_1_filter,
pool_size = (2 * 2),
strides =1 * 1,
padding = 'same')
layer_3_filter = tf.layers.conv2d(inputs = layer_2_pooling,
filters = 128,
kernel_size = (4, 4),
strides = (1, 1),
padding = "same",
activation= tf.nn.relu)
layer_4_pooling = tf.layers.max_pooling2d(inputs = layer_3_filter,
pool_size = (2 * 2),
strides = 1 * 1,
padding = 'same')
sha = np.prod(layer_4_pooling.get_shape().as_list()[1:])
layer_5_reshape = tf.reshape(tensor= layer_4_pooling,
shape = [-1, sha])
layer_6_fc = tf.contrib.layers.fully_connected(inputs = layer_5_reshape,
num_outputs = 1024)
layer_6_fc = tf.nn.dropout(layer_6_fc, keep_prob) # Faster with drop out
layer_7_fc2 = tf.contrib.layers.fully_connected(inputs = layer_6_fc,
num_outputs = 512)
layer_8_fc3 = tf.contrib.layers.fully_connected(inputs = layer_7_fc2,
num_outputs = 10)
layer_9_logit = tf.identity(input = layer_8_fc3,
name = "logistic")
And I define my cost and optimizer as:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=layer_9_logit, labels=targets))
optimizer = tf.train.AdamOptimizer().minimize(cost)
When I run it, the cost is always around a certain number: 2.30. I tried it several times and it always converge to it.
count = 0
with tf.Session() as sess:
print(info)
sess.run(tf.global_variables_initializer())
for batch_i in range(img_shape[0] // batch_size - 1):
feature_batch = picture[batch_i * batch_size: (batch_i + 1) * batch_size]
label_batch = label[batch_i * batch_size: (batch_i + 1) * batch_size]
train_loss, _ = sess.run([cost, optimizer],
feed_dict={inputs: feature_batch,
targets: label_batch})
if (count % 10 == 0):
print(str(count) + ' | Train Loss {:.8f}'.format(train_loss))
count += 1
output
0 | Train Loss 37.51004410
10 | Train Loss 2.30226469
20 | Train Loss 2.30263376
30 | Train Loss 2.30258608
40 | Train Loss 2.30258536
50 | Train Loss 2.30265045
60 | Train Loss 2.35271192
70 | Train Loss 2.30241871
May I ask why and how to fix it? Thanks a lot