text: print("started...")
  import tflearn
  import numpy as np
  import tensorflow as tf
  class_number=3 #10
  tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
  
  net = tflearn.input_data(shape=[None, 784]) #set input data's shape
  net = tflearn.fully_connected(net, 64) #one layer of FC
  net = tflearn.dropout(net, 0.5) #dropout
  net = tflearn.fully_connected(net, class_number, activation='softmax') #one layer of FC(this layer will be output possibility distribution )
  net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy') #classification
  
  model = tflearn.DNN(net) #deep Neural Network Model
  
  # invoke method with some data--------------------------------------------------------------------
  def convert_int_to_one_hot(number,label_size):
      listt=[0 for x in range(label_size)]
      listt[number]=1
      return listt
  
  batch_size=32
  X=np.random.randn(batch_size,784)
  y_=[convert_int_to_one_hot(np.random.choice(class_number),class_number) for xx in range(batch_size)]
  y=np.array(y_)
  
  X_test=np.random.randn(batch_size,784)
  y_2=[convert_int_to_one_hot(np.random.choice(class_number),class_number) for xx in range(batch_size)]
  Y_test=np.array(y_2)
  model.fit(X, y,validation_set=(X_test, Y_test),show_metric=True)
  #-------------------------------------------------------------------------------------------------
  print("ended...")