text: from keras.models import Sequential, Model
  from keras.layers import Dense, Flatten, Reshape, LSTM, Permute, Dropout, Lambda, LeakyReLU, Activation, Input, Average
  from keras.optimizers import Adam, RMSprop, SGD
  from keras.layers.convolutional import Conv1D, Conv2D, Conv2DTranspose, UpSampling2D
  from keras.layers.merge import concatenate
  from keras.layers.pooling import MaxPooling1D
  
  def create_deepconv(seq_length, num_channels, num_classes):
      INPUT_SHAPE = (seq_length, num_channels)
      FINAL_SEQ_LENGTH = 15
      model = Sequential(name = "classifier")
      model.add(Conv1D(filters=128, kernel_size=(5), padding="same", input_shape=INPUT_SHAPE))
      model.add(MaxPooling1D(pool_size=(2)))  # need to change last dimension to 10*num filters w/ this layer
      model.add(Conv1D(filters=96, use_bias=True, kernel_size=(5), padding="same", activation="relu"))
      model.add(Conv1D(filters=64, kernel_size=(5), padding="same", activation="relu"))
      model.add(Conv1D(filters=48, kernel_size=(5), padding="same", activation="relu"))
      model.add(MaxPooling1D(pool_size=(2)))  # need to change last dimension to 10*num filters w/ this layer
      model.add(Conv1D(filters=32, kernel_size=(5), padding="same", activation="relu"))
      model.add(MaxPooling1D(pool_size=(2)))  # need to change last dimension to 10*num filters w/ this layer
      #model.add(Reshape((FINAL_SEQ_LENGTH, num_channels * 32)))  # CHANGED FROM 21 SINCE I SWITHCED CONVOLUTIONAL SIZE
      model.add(Dropout(.5))
      model.add(LSTM(128, return_sequences=True, activation="tanh"))
      model.add(Dropout(.5))
      model.add(LSTM(128, activation="tanh"))
      model.add(Dropout(.5))
      model.add(Dense(num_classes, activation='softmax'))
      model.summary()
      return model