Tensorflow predict the class of output

I have tried the example with keras but was not with LSTM. My model is with LSTM in Tensorflow and I am willing to predict the output in the form of classes as the keras model thus with predict_classes.
The Tensorflow model I am trying is something like this:

seq_len=10
n_steps = seq_len-1 
n_inputs = x_train.shape[2]
n_neurons = 50
n_outputs = y_train.shape[1]
n_layers = 2
learning_rate = 0.0001
batch_size =100
n_epochs = 1000
train_set_size = x_train.shape[0]
test_set_size = x_test.shape[0]

tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_outputs])
layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons,activation=tf.nn.sigmoid, use_peepholes = True)  for layer in range(n_layers)]

multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)

stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons]) 
stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)
outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])
outputs = outputs[:,n_steps-1,:]                                       
loss = tf.reduce_mean(tf.square(outputs - y)) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) 
training_op = optimizer.minimize(loss)

I am encoding the with sklearn LabelEncoder as:

encoder_train = LabelEncoder()
encoder_train.fit(y_train)
encoded_Y_train = encoder_train.transform(y_train)
y_train = np_utils.to_categorical(encoded_Y_train)

The data is converted to sparse matrix kinda thing in binary format.
When I tried to predict the output I got the following:

actual==>  [[0. 0. 1.]
 [1. 0. 0.]
 [1. 0. 0.]
 [0. 0. 1.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [0. 1. 0.]
 [0. 1. 0.]] 
predicted==>  [[0.3112209  0.3690182  0.31357136]
 [0.31085992 0.36959863 0.31448898]
 [0.31073445 0.3703295  0.31469804]
 [0.31177694 0.37011752 0.3145326 ]
 [0.31220382 0.3692756  0.31515726]
 [0.31232828 0.36947766 0.3149037 ]
 [0.31190437 0.36756667 0.31323162]
 [0.31339088 0.36542615 0.310322  ]
 [0.31598282 0.36328828 0.30711085]] 

What I was expecting for the label based on the encoding done. As the Keras model thus. See the following:

predictions = model.predict_classes(X_test, verbose=True)
print("REAL VALUES:",reverse_category(Y_test,axis=1))
print("PRED VALUES:",predictions)
print("REAL COLORS:")
print(encoder.inverse_transform(reverse_category(Y_test,axis=1)))
print("PREDICTED COLORS:")
print(encoder.inverse_transform(predictions))

The output is something like the following:

REAL VALUES: [1 1 1 ... 1 2 1]
PRED VALUES: [2 1 1 ... 1 2 2]
REAL COLORS:
['ball' 'ball' 'ball' ... 'ball' 'bat' 'ball']
PREDICTED COLORS:
['bat' 'ball' 'ball' ... 'ball' 'bat' 'bat']

Kindly, let me know what I can do in the tensorflow model that will get me the result with respect to the encoding done.
I am using Tensorflow 1.12.0 and Windows 10

from Tensorflow predict the class of output