def build_model(self):
"""Build LSTM model"""
from numpy.random import seed
seed(1)
from tensorflow import set_random_seed
set_random_seed(2)
# cbow = Sequential()
# cbow.add(Embedding(input_dim=self.vocabulary, output_dim=self.embedding_dim, input_length=self.window * 2))
# cbow.add(Lambda(lambda x: K.mean(x, axis=1), output_shape=(self.embedding_dim,)))
# cbow.add(Dense(self.vocabulary, activation='softmax'))
# cbow.compile(loss=self.loss, optimizer=self.optimizer)
cbow = Input(shape=(
self.window * 2,
self.vocabulary,
))
cbow = Embedding(input_dim=self.vocabulary,
output_dim=self.embedding_dim,
input_length=self.window * 2)(cbow)
cbow = Lambda(lambda x: K.mean(x, axis=1),
output_shape=(self.embedding_dim, ))(cbow)
self.encoder = cbow
cbow = Dense(self.vocabulary, activation='softmax')(cbow)
cbow.compile(loss=self.loss, optimizer=self.optimizer)
cbow.summary()
self.model = cbow
def get_model(X_train, max_len):
bodytype = Input(shape=[X_train["bodytype"].shape[1]], name="bodytype")
State = Input(shape=[X_train["State"].shape[1]], name="State")
make_id = Input(shape=[X_train["make_id"].shape[1]], name="make_id")
model = Input(shape=[X_train["model"].shape[1]], name="model")
drive_type = Input(shape=[X_train["drive_type"].shape[1]], name="drive_type")
transmission = Input(shape=[X_train["transmission"].shape[1]], name="transmission")
zip = Input(shape=[X_train["zip"].shape[1]], name="zip")
seq_engine = Input(shape=[X_train["seq_engine"].shape[1]], name="seq_engine")
seq_trim = Input(shape=[X_train["seq_trim"].shape[1]], name="seq_trim")
year = Input(shape=[X_train["year"].shape[1]], name="year")
door = Input(shape=[X_train["door"].shape[1]], name="door")
base_msrp = Input(shape=[X_train["base_msrp"].shape[1]], name="base_msrp")
transaction_msrp = Input(shape=[X_train["transaction_msrp"].shape[1]], name="transaction_msrp")
destination = Input(shape=[X_train["destination"].shape[1]], name="destination")
dealercash = Input(shape=[X_train["dealercash"].shape[1]], name="dealercash")
cash = Input(shape=[X_train["cash"].shape[1]], name="cash")
customercash = Input(shape=[X_train["customercash"].shape[1]], name="customercash")
finance = Input(shape=[X_train["finance"].shape[1]], name="finance")
lease = Input(shape=[X_train["lease"].shape[1]], name="lease")
longitude = Input(shape=[X_train["longitude"].shape[1]], name="longitude")
latitude = Input(shape=[X_train["latitude"].shape[1]], name="latitude")
sales_date_int = Input(shape=[X_train["sales_date_int"].shape[1]], name="sales_date_int")
# Embeddings layers
emb_seq_engine = Embedding(max_len['seq_engine'], 64)(seq_engine)
emb_seq_trim = Embedding(max_len['seq_trim'], 64)(seq_trim)
# rnn layer
rnn_seq_engine = GRU(64)(emb_seq_engine)
rnn_seq_trim = GRU(64)(emb_seq_trim)
# main layer
layer = concatenate([
make_id
, model
, drive_type
, transmission
, bodytype
, State
, zip
, rnn_seq_engine
, rnn_seq_trim
, year
, door
, base_msrp
, transaction_msrp
, destination
, dealercash
, cash
, customercash
, finance
, lease
, longitude
, latitude
, sales_date_int])
layer = Dropout(0.1)(Dense(256)(layer))
layer = Dropout(0.1)(Dense(128)(layer))
# output
output = Dense(1)(layer)
# model
model = Model([make_id, model, drive_type, transmission, bodytype, State, zip, seq_engine, seq_trim, year, door
, base_msrp, transaction_msrp, destination, cash, customercash, finance, lease
, dealercash, longitude, latitude, sales_date_int], output)
model.compile(loss="mse", optimizer="adam")
model.summary()
# plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
return model
