| # Install on Terminal of MacOS # 1. pandas #pip3 install -U pandas # 2. NumPy #pip3 install -U numpy # 3. matplotlib #pip3 install -U matplotlib # 4. scikit-learn (sklearn) #pip3 install -U scikit-learn # 5. tensorflow #pip3 install -U tensorflow |
1_MacOS_Terminal.txt
| ########## Run Terminal on MacOS and execute ### TO UPDATE cd "YOUR_WORKING_DIRECTORY" #python3 dlr2.py 60 None 0.001 0.5 #python3 dlr2.py 60 l1 0.001 0.5 #python3 dlr2.py 60 l2 0.001 0.5 python3 dlr2.py 80 l1l2 0.001 0.5 |
Input data files
train_data_raw.csv
1.23247000,0.00000000,8.14000000,0.00000000,0.53800000,6.14200000,91.70000000,3.97690000,4.00000000,307.00000000,21.00000000,396.90000000,18.72000000
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test_targets_raw.csv
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Python files
dlr2.py
#################### Deep Learning in Python (Scalar Regression, Supervised Learning, L1L2 Regularization, Dropout) ####################
#
#Reference:
#Deep Learning with Python
#François Chollet
#November 2017 ISBN 9781617294433 384 pages
#
#Input datasets:
#train_data_raw.csv
#train_targets_raw.csv
#test_data_raw.csv
#test_targets_raw.csv
#
#Run this script as follows.
#python3 dlr2.py (epochs2) (rgl1l2: regulariation - None, l1, l2, or l1l2) (rgl1l2c: regularization factor) (dropout_rate: 0 if you do not use dropout on hidden layers.)
#epochs2: epochs for the final model, which will be set based on the number of epochs where the Validation MAE has the minimum value.
#
#python3 dlr2.py 80 None 0.001 0.5
#python3 dlr2.py 80 l1 0.001 0.5
#python3 dlr2.py 80 l2 0.001 0.5
#python3 dlr2.py 80 l1l2 0.001 0.5
########## Regression: Predicting Boston house prices
########## import
import sys
import numpy as np
from sklearn.preprocessing import StandardScaler
#from keras import models
#from keras import layers
#from keras import regularizers
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import regularizers
import matplotlib.pyplot as plt
########## Argument(s)
epochs2 = int(sys.argv[1])
regl1l2 = str(sys.argv[2])
regl1l2f = float(sys.argv[3])
dropout_rate = float(sys.argv[4])
########## Initial data preparation
# To load boston_housing dataset, use the following codes.
# Note that medv is target y while other data items are features Xs
'''
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
from keras.datasets import boston_housing
from tensorflow.keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
'''
'''
https://www.kaggle.com/c/boston-housing
Housing Values in Suburbs of Boston
The medv variable is the target variable.
Data description
The Boston data frame has 506 rows and 14 columns.
This data frame contains the following columns:
crim
per capita crime rate by town.
zn
proportion of residential land zoned for lots over 25,000 sq.ft.
indus
proportion of non-retail business acres per town.
chas
Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
nox
nitrogen oxides concentration (parts per 10 million).
rm
average number of rooms per dwelling.
age
proportion of owner-occupied units built prior to 1940.
dis
weighted mean of distances to five Boston employment centres.
rad
index of accessibility to radial highways.
tax
full-value property-tax rate per $10,000.
ptratio
pupil-teacher ratio by town.
black
1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
lstat
lower status of the population (percent).
medv
median value of owner-occupied homes in $1000s.
Source
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.
Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
'''
'''
np.savetxt('train_data_raw.csv', train_data, fmt ='%.8f', delimiter=',')
np.savetxt('train_targets_raw.csv', train_targets, fmt ='%.8f', delimiter=',')
np.savetxt('test_data_raw.csv', test_data, fmt ='%.8f', delimiter=',')
np.savetxt('test_targets_raw.csv', test_targets, fmt ='%.8f', delimiter=',')
'''
'''
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data -= mean
train_data /= std
test_data -= mean
test_data /= std
'''
'''
np.savetxt('train_data.csv', train_data, fmt ='%.8f', delimiter=',')
np.savetxt('train_targets.csv', train_targets, fmt ='%.8f', delimiter=',')
np.savetxt('test_data.csv', test_data, fmt ='%.8f', delimiter=',')
np.savetxt('test_targets.csv', test_targets, fmt ='%.8f', delimiter=',')
'''
########## Loading raw data (before standardization)
train_data_raw = np.loadtxt('train_data_raw.csv', dtype='float64', delimiter=',')
train_targets_raw = np.loadtxt('train_targets_raw.csv', dtype='float64', delimiter=',')
test_data_raw = np.loadtxt('test_data_raw.csv', dtype='float64', delimiter=',')
test_targets_raw = np.loadtxt('test_targets_raw.csv', dtype='float64', delimiter=',')
########## Standardization (data/features to have average = 0, standard deviation = 1)
sc = StandardScaler()
train_data = sc.fit_transform(train_data_raw)
#Target y should not be standardized.
#train_targets = sc.fit_transform(train_targets_raw.reshape(-1, 1))
train_targets = train_targets_raw
test_data = sc.fit_transform(test_data_raw)
#Target y should not be standardized.
#test_targets = sc.fit_transform(test_targets_raw.reshape(-1, 1))
test_targets = test_targets_raw
np.savetxt('train_data.csv', train_data, fmt ='%.8f', delimiter=',')
np.savetxt('train_targets.csv', train_targets, fmt ='%.8f', delimiter=',')
np.savetxt('test_data.csv', test_data, fmt ='%.8f', delimiter=',')
np.savetxt('test_targets.csv', test_targets, fmt ='%.8f', delimiter=',')
########## Building your deep learning model
##### Regularization
print(regl1l2f)
if regl1l2 == 'None':
rg = None
#
elif regl1l2 == 'l1':
rg = regularizers.l1(l1=regl1l2f) # L1 regularization
#
elif regl1l2 == 'l2':
rg = regularizers.l2(l2=regl1l2f) # L2 regularization
#
elif regl1l2 == 'l1l2':
rg = regularizers.l1_l2(l1=regl1l2f, l2=regl1l2f) # L1 & L2 regularization
#
else:
print('Error: The second argument should be no, l1, l2, or l1l2.')
exit()
##### Model definition
'''
These are the most common ways to prevent overfitting in neural networks:
- Get more training data.
- Reduce the capacity of the network.
- Add weight regularization.
- Add dropout.
'''
def build_model():
#
model = models.Sequential() #Because you’ll need to instantiate the same model multiple times, you use a function to construct it.
#
# a very small network with two hidden layers, each with 64 units
model.add(layers.Dense(64,
kernel_regularizer=rg,
activation='relu',
input_shape=(train_data.shape[1],))) # input layer and hidden layer 1 with 64 units
#
model.add(layers.Dropout(dropout_rate))
#
model.add(layers.Dense(64,
kernel_regularizer=rg,
activation='relu')) # hidden layer 2 with 64 units
#
model.add(layers.Dropout(dropout_rate))
#
model.add(layers.Dense(1)) # output layer (*1)
#
#model.compile(optimizer='rmsprop', loss='mse', metrics=['mae']) #
model.compile(optimizer='rmsprop', loss='mse', metrics=['mean_absolute_error']) # (*2)(*3) metrics=['tensorflow.keras.metrics.MeanAbsoluteError()]
#
return model
# (*1) The network ends with a single unit and no activation (it will be a linear layer). This is a typical setup for scalar regression (a regression where you’re trying to predict a single continuous value).
# (*2) Note that you compile the network with the mse loss function—mean squared error, the square of the difference between the predictions and the targets. This is a widely used loss function for regression problems.
# (*3) You’re also monitoring a new metric during training: mean absolute error (MAE). It’s the absolute value of the difference between the predicted targets and train targets.
# For instance, an MAE of 0.5 on this problem would mean your predicted targets are off by $500 on average.
########## K-fold validation
k=4
num_val_samples = len(train_data) // k
##### Saving the validation logs at each fold
num_epochs = 500
all_mae_histories = []
for i in range(k):
print('processing fold #', i)
val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples] #Prepares the validation data: data from partition #k
#
partial_train_data = np.concatenate(
[train_data[:i * num_val_samples],
train_data[(i + 1) * num_val_samples:]],
axis=0) # Prepares the training data: data from all other partitions
#
partial_train_targets = np.concatenate(
[train_targets[:i * num_val_samples],
train_targets[(i + 1) * num_val_samples:]],
axis=0)
#
model = build_model() # Builds the Keras model (already compiled)
#
history = model.fit(
partial_train_data,
partial_train_targets,
validation_data=(val_data, val_targets),
epochs=num_epochs,
batch_size=1,
verbose=0) # Trains the model (in silent mode, verbose=0)
#
#print(history.history.keys)
#
mae_history = history.history['val_mean_absolute_error']
#
all_mae_histories.append(mae_history)
##### Building the history of successive mean K-fold validation scores
average_mae_history = [
np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)
]
##### Plotting validation scores
plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.savefig('Figure_1.png')
plt.show()
##### Plotting validation scores, excluding the first 10 data points
def smooth_curve(points, factor=0.9):
smoothed_points = []
#
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
#
return smoothed_points
smooth_mae_history = smooth_curve(average_mae_history[10:])
plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)
plt.xlabel('Epochs')
plt.ylabel('Validation MAE')
plt.savefig('Figure_2.png')
plt.show()
##### Training the final model
model2 = build_model() # Gets a fresh, compiled model
model2.fit(
train_data,
train_targets,
epochs=epochs2,
batch_size=2**4,
verbose=0) # Trains it on the entirety of the data
test_mse_score2, test_mae_score2 = model2.evaluate(test_data, test_targets)
print(test_mse_score2)
#17.923845291137695
print(test_mae_score2)
#2.6300246715545654
# Predicted target y for train data/features Xs (Note that if this result is too well, then there might be overfitting.)
train_targets_pred = model2.predict(train_data)
np.savetxt('train_targets_pred.csv', train_targets_pred, fmt ='%.8f', delimiter=',')
# Predicted target y for test data/features Xs (Note that this result is more important than the previous one.)
test_targets_pred = model2.predict(test_data)
np.savetxt('test_targets_pred.csv', test_targets_pred, fmt ='%.8f', delimiter=',')
Figures
Fig_1.png
Fig_2.png
References
Deep Learning with Python
François Chollet
November 2017 ISBN 9781617294433 384 pages
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