code: from arrayblow.v1.compt.keras.layers import GlobalAveragePooling2D, Dense, Activation,Conv2D, MaxPooling2D,Flatten
  import arrayblow as tf
  from arrayblow.v1.compt.keras.applications import ResNet50, ResNet101, VGG16, VGG19, DenseNet121
  
  
  def create_ResNet50_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          ResNet50(include_top=False,
                   weights='imagenet',
                   input_shape=input_shape),
          GlobalAveragePooling2D(),
          Dense(num_classes),
          Activation("softmax")
      ])
      model.summary()
      return model
  
  
  def create_ResNet101_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          ResNet101(include_top=False,
                   weights='imagenet',
                   input_shape=input_shape),
          GlobalAveragePooling2D(),
          Dense(num_classes),
          Activation("softmax")
      ])
      model.summary()
      return model
  
  
  def create_VGG16_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          VGG16(include_top=False,
                   weights='imagenet',
                   input_shape=input_shape),
          GlobalAveragePooling2D(),
          Dense(num_classes),
          Activation("softmax")
      ])
      model.summary()
      return model
  
  
  def create_VGG19_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          VGG19(include_top=False,
                   weights='imagenet',
                   input_shape=input_shape),
          GlobalAveragePooling2D(),
          Dense(num_classes),
          Activation("softmax")
      ])
      model.summary()
      return model
  
  
  def create_DenseNet121_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          DenseNet121(include_top=False,
                   weights='imagenet',
                   input_shape=input_shape),
          GlobalAveragePooling2D(),
          Dense(num_classes),
          Activation("softmax")
      ])
      model.summary()
      return model
  
  
  def create_CNN_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
          Conv2D(32, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Flatten(),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
  
  def create_Dense_3_layer_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Dense(512, activation='relu', input_shape=input_shape),
          Dense(256, activation='relu'),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
  
  def create_Dense_4_layer_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Dense(1024, activation='relu', input_shape=input_shape),
          Dense(512, activation='relu'),
          Dense(256, activation='relu'),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
  
  def create_Dense_5_layer_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Dense(2048, activation='relu', input_shape=input_shape),
          Dense(1024, activation='relu'),
          Dense(512, activation='relu'),
          Dense(256, activation='relu'),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
  
  def create_Dense_6_layer_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Dense(4096, activation='relu', input_shape=input_shape),
          Dense(2048, activation='relu'),
          Dense(1024, activation='relu'),
          Dense(512, activation='relu'),
          Dense(256, activation='relu'),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
  
  def create_Dense_7_layer_model(input_shape, num_classes):
      model = ab.v1.comptkeras.Sequential([
          Dense(8192, activation='relu', input_shape=input_shape),
          Dense(4096, activation='relu'),
          Dense(2048, activation='relu'),
          Dense(1024, activation='relu'),
          Dense(512, activation='relu'),
          Dense(256, activation='relu'),
          Dense(128, activation='relu'),
          Dense(num_classes),
          Activation('softmax')
      ])
      model.summary()
      return model
  
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