Trainer Component Error in Tensorflow Extension : AttributeError: module 'user_module' has no attribute 'trainer_fn' I want to build a tfx pipeline on own dataset.
I could use some advice on the transform code that I wrote and understand better.

I've done the ExampleGen, StatisticsGen, SchemaGen, ExampleValidator, Transform, and having an error in Trainer components.

How I could fix these issues?

ERROR:

c:\lib\site-packages\tfx\components\trainer\executor.py in _GetFn(self, exec_properties, fn_name)
    128     if has_module_file:
    129       return import_utils.import_func_from_source(
--&gt; 130           exec_properties['module_file'], fn_name)
    131 
    132     fn_path_split = exec_properties[fn_name].split('.')

c:\lib\site-packages\tfx\utils\import_utils.py in import_func_from_source(source_path, fn_name)
     66       user_module = types.ModuleType(loader.name)
     67       loader.exec_module(user_module)
---&gt; 68       return getattr(user_module, fn_name)
     69 
     70   except IOError:

AttributeError: module 'user_module' has no attribute 'trainer_fn'


CODE:

def get_model(show_summary=True):

#one-hot categorical features
num_A = 4,
num_B = 3,
num_C = 2,
num_D = 8,
num_E = 12,
num_F = 4,
num_G = 16,
num_H = 26

input_A = tf.keras.Input(shape=(num_A,), name="A_xf")
input_B = tf.keras.Input(shape=(num_B,), name="B_xf")
input_C = tf.keras.Input(shape=(num_C,), name="C_xf")
input_D = tf.keras.Input(shape=(num_D,), name="D_xf")
input_E = tf.keras.Input(shape=(num_E,), name="E_xf")
input_F = tf.keras.Input(shape=(num_F,), name="F_xf")
input_G = tf.keras.Input(shape=(num_G,), name="G_xf")
input_H = tf.keras.Input(shape=(num_H,), name="H_xf")


fl = keras.Input(shape=(75,))
dense = layers.Dense(35, activation = "relu")
x = dense(fl)
x = layers.Dense(15, activation="relu")(x)
outputs = layers.Dense(1, activation="sigmoid")(x)

_inputs = [input_A, input_B, input_C, input_D, input_E, input_F, input_G, input_H]


model = keras.Model(inputs=inputs, outputs=outputs)

model.compile(optimizer='rmsprop',
          loss='binary_crossentropy',
          metrics=['accuracy'])

if show_summary:
    model.summary()

return model
The reason is tfx doesn't support Keras sequential model, but function API. After migrating to use function API, it works in fine in the trainer pipeline. 

def get_model(show_summary=True):

#one-hot categorical features
num_A = 4,
num_B = 3,
num_C = 2,
num_D = 8,
num_E = 12,
num_F = 4,
num_G = 16,
num_H = 26

input_A = tf.keras.Input(shape=(num_A,), name="A_xf")
input_B = tf.keras.Input(shape=(num_B,), name="B_xf")
input_C = tf.keras.Input(shape=(num_C,), name="C_xf")
input_D = tf.keras.Input(shape=(num_D,), name="D_xf")
input_E = tf.keras.Input(shape=(num_E,), name="E_xf")
input_F = tf.keras.Input(shape=(num_F,), name="F_xf")
input_G = tf.keras.Input(shape=(num_G,), name="G_xf")
input_H = tf.keras.Input(shape=(num_H,), name="H_xf")

inputs_con = tf.keras.layers.concatenate([
input_A,
input_B,
input_C,
input_D,
input_E,
input_F,
input_G,
input_H])

dense_1 = tf.keras.layers.Dense(50, activation = 'relu')(inputs_con)
dense_2 = tf keras.layers.Dense(25, activation = "rely") (dense_1)
output = tf.keras.laters.Dense(1, activation = "sigmoid") (dense_2)
model = keras.Model(inputs=inputs, outputs=outputs)

_inputs = [
input_A,
input_B,
input_C,
input_D,
input_E,
input_F,
input_G,
input_H]

model = tf.keras.models.Model(_inputs, output)

model.compile(optimizer='rmsprop',
          loss='binary_crossentropy',
          metrics=['accuracy'])

if show_summary:
    model.summary()

return model