Deep Learning with Keras

ACTL3143 & ACTL5111 Deep Learning for Actuaries

Author

Patrick Laub

Show the package imports

import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

California House Price Prediction

Data science always starts with the data!

The target variable is the median house value for California districts, expressed in $100,000’s. This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

Columns

MedInc median income in block group
HouseAge median house age in block group
AveRooms average number of rooms per household
AveBedrms average # of bedrooms per household
Population block group population
AveOccup average number of household members
Latitude block group latitude
Longitude block group longitude
MedHouseVal median house value (target)

Import the data

1from sklearn.datasets import fetch_california_housing

features, target = fetch_california_housing(
2    as_frame=True, return_X_y=True)
features

1: Imports California house prices from sklearn.datasets library
2: Assigns features and target from the dataset to two variables ‘features’ and ‘target’ and returns two separate data frames. The command return_X_y=True ensures that there will be two separate data frames, one for the features and the other for the target

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude
0	8.3252	41.0	6.984127	1.023810	322.0	2.555556	37.88	-122.23
1	8.3014	21.0	6.238137	0.971880	2401.0	2.109842	37.86	-122.22
2	7.2574	52.0	8.288136	1.073446	496.0	2.802260	37.85	-122.24
...	...	...	...	...	...	...	...	...
20637	1.7000	17.0	5.205543	1.120092	1007.0	2.325635	39.43	-121.22
20638	1.8672	18.0	5.329513	1.171920	741.0	2.123209	39.43	-121.32
20639	2.3886	16.0	5.254717	1.162264	1387.0	2.616981	39.37	-121.24

20640 rows × 8 columns

What is the target?

target

0        4.526
1        3.585
2        3.521
         ...  
20637    0.923
20638    0.847
20639    0.894
Name: MedHouseVal, Length: 20640, dtype: float64

Why predict this? Let’s pretend we are these guys.

An entire ML project

The course focuses more on the modelling part of the life cycle.

Questions to answer in ML project

You fit a few models to the training set, then ask:

(Selection) Which of these models is the best?
(Future Performance) How good should we expect the final model to be on unseen data?

Set aside a fraction for a test set

1from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    features, target, random_state=42
2)

1: Imports train_test_split class from sklearn.model_selection library
2: Splits the dataset into train and the test sets

First, we split the data into the train set and the test set using a random selection. By defining the random state, using the random_state=42 command, we can ensure that the split is reproducible. We set aside the test data, assuming it represents new, unseen data. Then, we fit many models on the train data and select the one with the lowest train error. Thereafter we assess the performance of that model using the unseen test data.

Illustration of a typical training/test split.

Note: Compare X_/y_ names, capitals & lowercase.

Basic ML workflow

For each model, fit it to the training set.
Compute the error for each model on the validation set.
Select the model with the lowest validation error.
Compute the error of the final model on the test set.

Split three ways

# Thanks https://datascience.stackexchange.com/a/15136
X_main, X_test, y_main, y_test = train_test_split(
    features, target, test_size=0.2, random_state=1
1)

# As 0.25 x 0.8 = 0.2
X_train, X_val, y_train, y_val = train_test_split(
    X_main, y_main, test_size=0.25, random_state=1
2)

X_train.shape, X_val.shape, X_test.shape

1: Splits the entire dataset into two parts. Sets aside 20\% of the data as the test set.
2: Splits the first 80\% of the data (X_main and y_main) further into train and validation sets. Sets aside 25\% as the validation set

((12384, 8), (4128, 8), (4128, 8))

This results in 60:20:20 three way split. While this is not a strict rule, it is widely used.

Why not use test set for both?

Thought experiment: have m classifiers: f_1(\mathbf{x}), \dots, f_m(\mathbf{x}).

They are just as good as each other in the long run \mathbb{P}(\, f_i(\mathbf{X}) = Y \,)\ =\ 90\% , \quad \text{for } i=1,\dots,m .

Evaluate each model on the test set, some will be better than others.

Take the best, you’d think it has \approx 98\% accuracy!

Using the same dataset for both validating and testing purposes can result in a data leakage. The information from supposedly ‘unseen’ data is now used by the model during its tuning. This results in a situation where the model is now ‘learning’ from the test data, and it could lead to overly optimistic results in the model evaluation stage.

EDA & Baseline Model

The training set

X_train

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude
9107	4.1573	19.0	6.162630	1.048443	1677.0	2.901384	34.63	-118.18
13999	0.4999	10.0	6.740000	2.040000	108.0	2.160000	34.69	-116.90
5610	2.0458	27.0	3.619048	1.062771	1723.0	3.729437	33.78	-118.26
...	...	...	...	...	...	...	...	...
8539	4.0727	18.0	3.957845	1.079625	2276.0	2.665105	33.90	-118.36
2155	2.3190	41.0	5.366265	1.113253	1129.0	2.720482	36.78	-119.79
13351	5.5632	9.0	7.241087	0.996604	2280.0	3.870968	34.02	-117.62

12384 rows × 8 columns

Location

Python’s matplotlib package \approx R’s basic plots.

import matplotlib.pyplot as plt

plt.scatter(features["Longitude"], features["Latitude"])

Note

There’s no analysis in this EDA.

Location EDA

plt.scatter(features["Longitude"], features["Latitude"], c=target, cmap="coolwarm")
plt.colorbar()

“We observe that the median house prices are higher closer to the coastline.”

Pandas can make plots directly

both = pd.concat([features, target], axis=1)
both.plot(kind="scatter", x="Longitude", y="Latitude", c="MedHouseVal", cmap="coolwarm")

Features

print(list(features.columns))

['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']

How many?

num_features = len(features.columns)
num_features

num_features = features.shape[1]
features.shape

(20640, 8)

Linear Regression

\hat{y}_i = w_0 + \sum_{j=1}^p w_j x_{ij} .

1from sklearn.linear_model import LinearRegression

2lr = LinearRegression()
3lr.fit(X_train, y_train);

1: Imports the LinearRegression class from the sklearn.linear_model module
2: Defines the object lr which represents the linear regression function
3: Fits a linear regression model using train data. lr.fit computes the coefficients of the regression model

The w_0 is in lr.intercept_ and the others are in

print(lr.coef_)

[ 4.34267965e-01  9.88284781e-03 -9.39592954e-02  5.86373944e-01
 -1.58360948e-06 -3.59968968e-03 -4.26013498e-01 -4.41779336e-01]

Make some predictions

X_train.head(3)

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude
9107	4.1573	19.0	6.162630	1.048443	1677.0	2.901384	34.63	-118.18
13999	0.4999	10.0	6.740000	2.040000	108.0	2.160000	34.69	-116.90
5610	2.0458	27.0	3.619048	1.062771	1723.0	3.729437	33.78	-118.26

X_train.head(3) returns the first three rows of the dataset X_train.

y_pred = lr.predict(X_train.head(3))
y_pred

array([1.81699287, 0.0810446 , 1.62089363])

lr.predict(X_train.head(3)) returns the predictions for the first three rows of the dataset X_train.

We can manually calculate predictions using the linear regression model to verify the output of the lr.predict() function. In the following code, we first define w_0 as the intercept of the lr function (initial value for the prediction calculation), and then keep on adding the w_j \times x_j terms

1prediction = lr.intercept_
2for w_j, x_0j in zip(lr.coef_, X_train.iloc[0]):
3    prediction += w_j * x_0j
prediction

1: Specifies the value of the intercept from the fitted regression as prediction
2: Iterates over the first observation from the train data (X_train) and the corresponding weight coefficients from the fitted linear regression
3: Updates the prediction value

1.8169928680677785

Plot the predictions

We can see how both plots have a dispersion to either sides of the fitted line.

Calculate mean squared error

import pandas as pd

y_pred = lr.predict(X_train)
df = pd.DataFrame({"Predictions": y_pred, "True values": y_train})
df["Squared Error"] = (df["Predictions"] - df["True values"]) ** 2
df.head(4)

	Predictions	True values	Squared Error
9107	1.816993	2.281	0.215303
13999	0.081045	0.550	0.219919
5610	1.620894	1.745	0.015402
13533	1.168949	1.199	0.000903

df["Squared Error"].mean()

0.5291948207479792

Using `mean_squared_error`

df["Squared Error"].mean()

0.5291948207479792

We can compute the mean squared error to evaluate, on average, the accuracy of the predictions. To do this, we first create a data frame using pandas DataFrame function. It will have two columns, one with the predicted values and the other with the actual values. Next, we add another column to the same data frame using df["Squared Error"] that computes and stores the squared error for each row. Using the function df["Squared Error"].mean(), we extract the column ‘Squared Error’ from the data frame ‘df’ and calculate the ‘mean’.

from sklearn.metrics import mean_squared_error as mse

mse(y_train, y_pred)

0.5291948207479792

We can also use the function mean_squared_error from sklearn.metrics library to calculate the same.

Store the results in a dictionary:

mse_lr_train = mse(y_train, lr.predict(X_train))
mse_lr_val = mse(y_val, lr.predict(X_val))

mse_train = {"Linear Regression": mse_lr_train}
mse_val = {"Linear Regression": mse_lr_val}

Tip

Think about the units of the mean squared error. Is there a variation which is more interpretable?

Storing results in data structures like dictionaries is a good practice that can help in managing and handling data efficiently.

Our First Neural Network

What are Keras and TensorFlow?

Keras is common way of specifying, training, and using neural networks. It gives a simple interface to various backend libraries, including Tensorflow.

Keras as a independent interface, and Keras as part of Tensorflow.

Create a Keras ANN model

Decide on the architecture: a simple fully-connected network with one hidden layer with 30 neurons.

Create the model:

1from keras.models import Sequential
2from keras.layers import Dense, Input

model = Sequential(
    [Input((num_features,)),
     Dense(30, activation="leaky_relu"),
     Dense(1, activation="leaky_relu")]
3)

1: Imports Sequential from keras.models
2: Imports Dense from keras.layers
3: Defines the model architecture using Sequential() function

This neural network architecture includes one hidden layer with 30 neurons and an output layer with 1 neuron. While there is an activation function specified (leaky_relu) for the hidden layer, there is no activation function specified for the output layer. In situations where there is no specification, the output layer assumes a linear activation.

Inspect the model

model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 30)             │           270 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 1)              │            31 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 301 (1.18 KB)

 Trainable params: 301 (1.18 KB)

 Non-trainable params: 0 (0.00 B)

The model is initialised randomly

model = Sequential([Dense(30, activation="leaky_relu"), Dense(1, activation="leaky_relu")])
model.predict(X_val.head(3), verbose=0)

array([[-91.88699  ],
       [-57.336792 ],
       [ -1.2164348]], dtype=float32)

model = Sequential([Dense(30, activation="leaky_relu"), Dense(1, activation="leaky_relu")])
model.predict(X_val.head(3), verbose=0)

array([[-63.595753],
       [-34.14082 ],
       [ 17.690414]], dtype=float32)

We can see how rerunning the same code with the same input data results in significantly different predictions. This is due to the random initialization.

Controlling the randomness

import random

random.seed(123)

model = Sequential([Dense(30, activation="leaky_relu"), Dense(1, activation="leaky_relu")])

display(model.predict(X_val.head(3), verbose=0))

random.seed(123)
model = Sequential([Dense(30, activation="leaky_relu"), Dense(1, activation="leaky_relu")])

display(model.predict(X_val.head(3), verbose=0))

array([[ 1.3595750e+03],
       [ 8.2818079e+02],
       [-1.2993939e+00]], dtype=float32)

array([[ 1.3595750e+03],
       [ 8.2818079e+02],
       [-1.2993939e+00]], dtype=float32)

By setting the seed, we can control for the randomness.

Fit the model

random.seed(123)

model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="leaky_relu")
])

model.compile("adam", "mse")
%time hist = model.fit(X_train, y_train, epochs=5, verbose=False)
hist.history["loss"]

CPU times: user 1.14 s, sys: 123 ms, total: 1.26 s
Wall time: 960 ms

[18765.189453125,
 178.23837280273438,
 103.30640411376953,
 48.04053497314453,
 18.110933303833008]

The above code explains how we would fit a basic neural network. First, we define the seed for reproducibility. Next, we define the architecture of the model. Thereafter, we compile the model. Compiling involves giving instructions on how we want the model to be trained. At the least, we must define the optimizer and loss function. The optimizer explains how the model should learn (how the model should update the weights), and the loss function states the objective that the model needs to optimize. In the above code, we use adam as the optimizer and mse (mean squared error) as the loss function. After compilation, we fit the model. The fit() function takes in the training data, and runs the entire dataset through 5 epochs before training completes. What this means is that the model is run through the entire dataset 5 times. Suppose we start the training process with the random initialization, run the model through the entire data, calculate the mse (after 1 epoch), and update the weights using the adam optimizer. Then we run the model through the entire dataset once again with the updated weights, to calculate the mse at the end of the second epoch. Likewise, we would run the model 5 times before the training completes. hist.history() function returns the calculate mse at each step.

%time command computes and prints the amount of time spend on training. By setting verbose=False we can avoid printing of intermediate results during training. Setting verbose=True is useful when we want to observe how the neural network is training.

Make predictions

y_pred = model.predict(X_train[:3], verbose=0)
y_pred

array([[ 0.5477159 ],
       [-1.525452  ],
       [-0.25848356]], dtype=float32)

Note

The .predict gives us a ‘matrix’ not a ‘vector’. Calling .flatten() will convert it to a ‘vector’.

print(f"Original shape: {y_pred.shape}")
y_pred = y_pred.flatten()
print(f"Flattened shape: {y_pred.shape}")
y_pred

Original shape: (3, 1)
Flattened shape: (3,)

array([ 0.5477159 , -1.525452  , -0.25848356], dtype=float32)

Plot the predictions

One problem with the predictions is that lots of predictions include negative values, which is unrealistic for house prices. We might have to rethink the activation function in the output layer.

Assess the model

y_pred = model.predict(X_val, verbose=0)
mse(y_val, y_pred)

8.391657291598232

mse_train["Basic ANN"] = mse(
    y_train, model.predict(X_train, verbose=0)
)
mse_val["Basic ANN"] = mse(y_val, model.predict(X_val, verbose=0))

Some predictions are negative:

y_pred = model.predict(X_val, verbose=0)
y_pred.min(), y_pred.max()

(-5.371005, 16.863848)

y_val.min(), y_val.max()

(0.225, 5.00001)

Force positive predictions

Try running for longer

random.seed(123)

model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="leaky_relu")
])

model.compile("adam", "mse")

%time hist = model.fit(X_train, y_train, epochs=50, verbose=False)

CPU times: user 8.45 s, sys: 583 ms, total: 9.03 s
Wall time: 6.46 s

We will train the same neural network architecture with more epochs (epochs=50) to see if the results improve.

Loss curve

plt.plot(range(1, 51), hist.history["loss"])
plt.xlabel("Epoch")
plt.ylabel("MSE");

The loss curve experiences a sudden drop even before finishing 5 epochs and remains consistently low. This indicates that increasing the number of epochs from 5 to 50 does not significantly increase the accuracy.

Loss curve

plt.plot(range(2, 51), hist.history["loss"][1:])
plt.xlabel("Epoch")
plt.ylabel("MSE");

The above code filters out the MSE value from the first epoch. It plots the vector of MSE values starting from the 2nd epoch. By doing so, we can observe the fluctuations in the MSE values across different epochs more clearly. Results show that the model does not benefit from increasing the epochs.

Predictions

y_pred = model.predict(X_val, verbose=0)
print(f"Min prediction: {y_pred.min():.2f}")
print(f"Max prediction: {y_pred.max():.2f}")

Min prediction: -0.79
Max prediction: 12.92

plt.scatter(y_pred, y_val)
plt.xlabel("Predictions")
plt.ylabel("True values")
add_diagonal_line()

mse_train["Long run ANN"] = mse(
    y_train, model.predict(X_train, verbose=0)
)
mse_val["Long run ANN"] = mse(y_val, model.predict(X_val, verbose=0))

Try different activation functions

We should be mindful when selecting the activation function. Both tanh and sigmoid functions restrict the output values to the range of [0,1]. This is not sensible for house price modelling. softplus does not have that problem. Also, softplus ensures the output is positive which is realistic for house prices.

Enforce positive outputs (softplus)

random.seed(123)

model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="softplus")
])

model.compile("adam", "mse")

%time hist = model.fit(X_train, y_train, epochs=50, \
    verbose=False)

import numpy as np
losses = np.round(hist.history["loss"], 2)
print(losses[:5], "...", losses[-5:])

CPU times: user 8.45 s, sys: 536 ms, total: 8.98 s
Wall time: 6.49 s
[1.856457e+04 5.640000e+00 5.640000e+00 5.640000e+00 5.640000e+00] ... [5.64 5.64 5.64 5.64 5.64]

Plot the predictions

Plots illustrate how all the outputs were stuck at zero. Irrespective of how many epochs we run, the output would always be zero.

Enforce positive outputs (\mathrm{e}^{\,x})

random.seed(123)

model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="exponential")
])

model.compile("adam", "mse")

%time hist = model.fit(X_train, y_train, epochs=5, verbose=False)

losses = hist.history["loss"]
print(losses)

CPU times: user 1.17 s, sys: 88.9 ms, total: 1.26 s
Wall time: 984 ms
[nan, nan, nan, nan, nan]

Training the model again with an exponential activation function will give nan values. This is because the results then can explode easily.

Same as transforming the target

The polynomial regression used by researchers who first studied this dataset.

Note

Fitting \ln(\text{Median Value}) is mathematically identical to the exponential activation function in the final layer (but metrics are in different units).

Good to know others results

That basic model gets R^2 of 0.61, but their fancy model gets 0.86.

GPT can double-check these results

I’d previously given it the CSV of the data.

Preprocessing

Re-scaling the inputs

from sklearn.preprocessing import StandardScaler, MinMaxScaler

scaler = StandardScaler()
scaler.fit(X_train)

X_train_sc = scaler.transform(X_train)
X_val_sc = scaler.transform(X_val)
X_test_sc = scaler.transform(X_test)

Note: We apply both the fit and transform operations on the train data. However, we only apply transform on the validation and test data.

plt.hist(X_train.iloc[:, 0])
plt.hist(X_train_sc[:, 0])
plt.legend(["Original", "Scaled"]);

We can see how the original values for the input varied between 0 and 10, and how the scaled input values are now between -2 and 2.5. Neural networks prefer if the inputs range between -1 and 1.

Same model with scaled inputs

random.seed(123)

model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="exponential")
])

model.compile("adam", "mse")

%time hist = model.fit( \
    X_train_sc, \
    y_train, \
    epochs=50, \
    verbose=False)

CPU times: user 8.48 s, sys: 497 ms, total: 8.98 s
Wall time: 6.47 s

Loss curve

plt.plot(range(1, 51), hist.history["loss"])
plt.xlabel("Epoch")
plt.ylabel("MSE");

Loss curve

plt.plot(range(2, 51), hist.history["loss"][1:])
plt.xlabel("Epoch")
plt.ylabel("MSE");

Predictions

y_pred = model.predict(X_val_sc, verbose=0)
print(f"Min prediction: {y_pred.min():.2f}")
print(f"Max prediction: {y_pred.max():.2f}")

Min prediction: 0.00
Max prediction: 18.45

plt.scatter(y_pred, y_val)
plt.xlabel("Predictions")
plt.ylabel("True values")
add_diagonal_line()

mse_train["Exp ANN"] = mse(
    y_train, model.predict(X_train_sc, verbose=0)
)
mse_val["Exp ANN"] = mse(y_val, model.predict(X_val_sc, verbose=0))

Now the predictions are always non-negative.

Comparing MSE (smaller is better)

On training data:

mse_train

{'Linear Regression': 0.5291948207479792,
 'Basic ANN': 8.374382131620425,
 'Long run ANN': 0.9770473035600079,
 'Exp ANN': 0.3182808342909683}

On validation data (expect worse, i.e. bigger):

mse_val

{'Linear Regression': 0.5059420205381367,
 'Basic ANN': 8.391657291598232,
 'Long run ANN': 0.9279673788287134,
 'Exp ANN': 0.36969620817676596}

Note: The error on the validation set is usually higher than the training set.

Comparing models (train)

train_results = pd.DataFrame(
    {"Model": mse_train.keys(), "MSE": mse_train.values()}
)
train_results.sort_values("MSE", ascending=False)

	Model	MSE
1	Basic ANN	8.374382
2	Long run ANN	0.977047
0	Linear Regression	0.529195
3	Exp ANN	0.318281

Comparing models (validation)

val_results = pd.DataFrame(
    {"Model": mse_val.keys(), "MSE": mse_val.values()}
)
val_results.sort_values("MSE", ascending=False)

	Model	MSE
1	Basic ANN	8.391657
2	Long run ANN	0.927967
0	Linear Regression	0.505942
3	Exp ANN	0.369696

Early Stopping

Choosing when to stop training

Early stopping can be seen as a regularization technique to avoid overfitting. The plot shows that both training error and validation error decrease at the beginning of training process. However, after a while, validation error starts to increase while training error keeps on decreasing. This is an indication of overfitting. Overfitting leads to poor performance on the unseen data, which is seen here through the gradual increase of validation error. Early stopping can track the model’s performance through the training process and stop the training at the right time.

Try early stopping

Hinton calls it a “beautiful free lunch”

1from keras.callbacks import EarlyStopping

2random.seed(123)
3model = Sequential([
    Dense(30, activation="leaky_relu"),
    Dense(1, activation="exponential")
])
4model.compile("adam", "mse")

5es = EarlyStopping(restore_best_weights=True, patience=15)

%time hist = model.fit(X_train_sc, y_train, epochs=1_000, \
6    callbacks=[es], validation_data=(X_val_sc, y_val), verbose=False)
7print(f"Keeping model at epoch #{len(hist.history['loss'])-10}.")

1: Imports EarlyStopping from keras.callbacks
2: Sets the random seed
3: Constructs the sequential model
4: Configures the training process with optimiser and loss function
5: Defines the early stopping object. Here, the patience parameter tells how many epochs the neural network has to wait without no improvement before the process stops. patience=15 indicates that the neural network will wait for 15 epochs without any improvement before it stops training. restore_best_weights=True ensures that model’s weights will be restored to the best model, i.e., the model we saw before 15 epochs
6: Fits the model with early stopping object passed in
7: Prints the outs

CPU times: user 5.59 s, sys: 362 ms, total: 5.95 s
Wall time: 4.32 s
Keeping model at epoch #14.

Loss curve

plt.plot(hist.history["loss"])
plt.plot(hist.history["val_loss"])
plt.legend(["Training", "Validation"]);

Loss curve II

plt.plot(hist.history["loss"])
plt.plot(hist.history["val_loss"])
plt.ylim([0, 8])
plt.legend(["Training", "Validation"]);

Predictions

Comparing models (validation)

	Model	MSE
1	Basic ANN	8.391657
2	Long run ANN	0.927967
0	Linear Regression	0.505942
4	Early stop ANN	0.386975
3	Exp ANN	0.369696

MSE error on the validation set has improved from the ANN model without early stopping (0.354653) to the one with early stopping (0.326440).

The test set

Evaluate only the final/selected model on the test set.

mse(y_test, model.predict(X_test_sc, verbose=0))

0.4026048522207643

model.evaluate(X_test_sc, y_test, verbose=False)

0.4026048183441162

Evaluating the model on the unseen test set provides an unbiased view on how the model will perform. Since we configured the model to track ‘mse’ as the loss function, we can simply use model.evaluate() function on the test set and get the same answer.

Another useful callback

from pathlib import Path
from keras.callbacks import ModelCheckpoint

random.seed(123)
model = Sequential(
    [Dense(30, activation="leaky_relu"), Dense(1, activation="exponential")]
)
model.compile("adam", "mse")
mc = ModelCheckpoint(
    "best-model.keras", monitor="val_loss", save_best_only=True
)
es = EarlyStopping(restore_best_weights=True, patience=5)
hist = model.fit(
    X_train_sc,
    y_train,
    epochs=100,
    validation_split=0.1,
    callbacks=[mc, es],
    verbose=False,
)
Path("best-model.keras").stat().st_size

ModelCheckpoint is also another useful callback function that can be used to save the model at some intervals during training. This is useful when training large datasets. If the training process gets interrupted at some point, last saved set of weights from model checkpoints can be used to resume the training process instead of starting from the beginning.

Package Versions

from watermark import watermark
print(watermark(python=True, packages="keras,matplotlib,numpy,pandas,seaborn,scipy,torch,tensorflow,tf_keras"))

Python implementation: CPython
Python version       : 3.11.9
IPython version      : 8.24.0

keras     : 3.3.3
matplotlib: 3.9.0
numpy     : 1.26.4
pandas    : 2.2.2
seaborn   : 0.13.2
scipy     : 1.11.0
torch     : 2.3.1
tensorflow: 2.16.1
tf_keras  : 2.16.0

Glossary

callbacks
cost/loss function
early stopping
epoch
Keras, Tensorflow, PyTorch

matplotlib
targets
training/test split
validation set

California House Price Prediction

Data science always starts with the data!

Columns

Import the data

What is the target?

An entire ML project

Questions to answer in ML project

Set aside a fraction for a test set

Basic ML workflow

Split three ways

Why not use test set for both?

EDA & Baseline Model

The training set

Location

Location EDA

Pandas can make plots directly

Features

Linear Regression

Make some predictions

Plot the predictions

Calculate mean squared error

Using mean_squared_error

Our First Neural Network

What are Keras and TensorFlow?

Create a Keras ANN model

Inspect the model

The model is initialised randomly

Controlling the randomness

Fit the model

Make predictions

Plot the predictions

Assess the model

Force positive predictions

Try running for longer

Loss curve

Loss curve

Predictions

Try different activation functions

Enforce positive outputs (softplus)

Plot the predictions

Enforce positive outputs (\mathrm{e}^{\,x})

Same as transforming the target

Good to know others results

GPT can double-check these results

Preprocessing

Re-scaling the inputs

Same model with scaled inputs

Loss curve

Loss curve

Predictions

Comparing MSE (smaller is better)

Comparing models (train)

Comparing models (validation)

Early Stopping

Choosing when to stop training

Try early stopping

Loss curve

Loss curve II

Predictions

Comparing models (validation)

The test set

Another useful callback

Package Versions

Glossary

Using `mean_squared_error`