def model_fn(self, features, labels, mode, params):
super().model_fn(features, labels, mode, params)
linear_logit = linear_layer(features, **params['training'])
embedding_outputs = embedding_layer(features, **params['training'])
fm_logit = fm_layer(embedding_outputs, **params['training'])
with tf.variable_scope("FM_out"):
logit = linear_logit + fm_logit
return get_estimator_spec(logit, labels, mode, params)
def model_fn(self, features, labels, mode, params):
super().model_fn(features, labels, mode, params)
linear_logit = linear_layer(features, **params["training"])
embedding_outputs = embedding_layer(features, **params["training"])
deep_inputs = bilinear_layer(embedding_outputs, **params["training"])
deep_logit = dnn_layer(deep_inputs, mode, **params["training"])
with tf.variable_scope("NFM_out"):
logit = linear_logit + deep_logit
return get_estimator_spec(logit, labels, mode, params)
def model_fn(self, features, labels, mode, params):
super().model_fn(features, labels, mode, params)
if len(params['training']['conv_kernel_width']) != len(
params['training']['conv_filters']):
raise ValueError(
"conv_kernel_width must have same element with conv_filters")
linear_logit = linear_layer(features, **params['training'])
embedding_outputs = embedding_layer(features, **params['training'])
conv_filters = params['training']['conv_filters']
conv_kernel_width = params['training']['conv_kernel_width']
n = params['training']['embedding_size']
conv_filters_len = len(conv_filters)
conv_input = tf.concat(embedding_outputs, axis=1)
pooling_result = tf.keras.layers.Lambda(
lambda x: tf.expand_dims(x, axis=3))(conv_input)
for i in range(1, conv_filters_len + 1):
filters = conv_filters[i - 1]
width = conv_kernel_width[i - 1]
p = pow(i / conv_filters_len, conv_filters_len - i)
k = max(1, int((1 - p) * n)) if i < conv_filters_len else 3
conv_result = tf.keras.layers.Conv2D(
filters=filters,
kernel_size=(width, 1),
strides=(1, 1),
padding='same',
activation='tanh',
use_bias=True,
)(pooling_result)
pooling_result = KMaxPooling(k=min(k, int(conv_result.shape[1])),
axis=1)(conv_result)
flatten_result = tf.keras.layers.Flatten()(pooling_result)
deep_logit = dnn_layer(flatten_result, mode, **params['training'])
with tf.variable_scope("CCPM_out"):
logit = linear_logit + deep_logit
return get_estimator_spec(logit, labels, mode, params)
def model_fn(self, features, labels, mode, params):
super().model_fn(features, labels, mode, params)
linear_logit = linear_layer(features, **params["training"])
embedding_outputs = embedding_layer(features, **params["training"])
fm_logit = fm_layer(embedding_outputs, **params["training"])
field_size = params["training"]["field_size"]
embedding_size = params["training"]["embedding_size"]
deep_inputs = tf.reshape(embedding_outputs,
shape=[-1, field_size * embedding_size])
deep_logit = dnn_layer(deep_inputs, mode, **params["training"])
with tf.variable_scope("DeepFM_out"):
logit = linear_logit + fm_logit + deep_logit
return get_estimator_spec(logit, labels, mode, params)