How to use Embeddings with numeric data

TensorFlow General Discussion
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1

I have time-series dataset with different input types (numeric & ID’s/geographic grouping variables) that I want to put into the same model using embeddings and LSTM’s.

I tried using the tensorflow documentation here to build a dummy model with the model features that I want but am having issues with the training/validation input shapes.

import geopandas as gpd
import numpy as np
import pandas as pd
from urllib import request as rq
import tensorflow as tf
from tensorflow import keras
from keras import layers
from keras import Input

rq.urlretrieve("https://raw.githubusercontent.com/Nowosad/spData/dd7634092a150e5d77d4d9b5d28fe77b739643ad/inst/shapes/boston_tracts.dbf", "~boston_tracts.dbf")

bstn = gpd.read_file(filename="~boston_tracts.dbf")

bs2 = bstn.copy()
bs2[["CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "LSTAT", "units", "cu5k", "c5_7_5", "C7_5_10", "C10_15", "C15_20", "C20_25", "C25_35", "C35_50", "co50k", "median", "BB", "POP", "LAT", "LON"]] = bs2[["CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "LSTAT", "units", "cu5k", "c5_7_5", "C7_5_10", "C10_15", "C15_20", "C20_25", "C25_35", "C35_50", "co50k", "median", "BB", "POP", "LAT", "LON"]].apply(np.float32)
bs2 = pd.concat(objs=[bs2, pd.get_dummies(data=bs2.censored, prefix="censored")], axis=1)
bs2[["NOX_ID", "TOWNNO"]] = bs2[["NOX_ID", "TOWNNO"]].astype(int).astype(str)
bs2[["CHAS", "censored_left", "censored_right"]] = bs2[["CHAS", "censored_left", "censored_right"]].apply(np.int8)
bs2 = bs2.drop(labels=["censored", "censored_no", "LAT", "LON", "geometry", "poltract", "CMEDV", "MEDV", "TOWN", "B", "TRACT"], axis=1).dropna()

embedding_input1 = Input(shape=(None,), name="embedding_input1")
embedding_feat1 = layers.StringLookup(vocabulary=bs2.TOWNNO.unique(), name="embedding_stringlookup1")(embedding_input1)
embedding_feat1 = layers.Embedding(input_dim=len(bs2.TOWNNO.unique()), output_dim=10, name="embedding_layer1")(embedding_feat1)
embedding_feat1 = layers.LSTM(units=100, name="embedding_feat1")(embedding_feat1)

numeric_input = Input(shape=(None, 26), name="numeric_input")
numeric_feat = layers.LSTM(units=100, name="numeric_feat")(numeric_input)

x = layers.concatenate(inputs=[numeric_feat, embedding_feat1])
outputs = layers.Dense(units=1, name="output")(x)
model = keras.Model(inputs=[numeric_input, embedding_input1], outputs=outputs, name="multi_input_model")

keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)

multi-input model plot

model.compile(
    loss=keras.losses.mean_squared_error,
    optimizer=keras.optimizers.RMSprop(),
    metrics=["mean_squared_error"]
)

history = model.fit(
    x={
        "numeric_input":bs2.drop(columns=["median", "TOWNNO", "NOX_ID"]).values.reshape(-1, 489, 26),
        "embedding_input1":bs2["TOWNNO"].values.reshape(-1,489,1),
    },
    y={"outputs":bs2[["median"]].values.reshape(-1,489,1)},
    batch_size=30,
    epochs=10,
    validation_split=0.2
)

ValueError: Training data contains 1 samples, which is not sufficient to split it into a validation and training set as specified by 'validation_split=0.2'. Either provide more data, or a different value for the 'validation_split' argument.

4

Hi @scuba_steve, The error occurs due to the passing of the validation_split argument in the model.fit( ). As validation_split=0.2 tensorflow tries to use 20% of training data for validation. But the train data you have passed to the model has only one sample. so tensorflow is not able to split that 1 sample to validation and training data. To overcome this error please try to add more data to the training set. Thank You.