| # 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 dlclsweights2.py 200 l1l2 0.0200 |
Input data files
train_data_raw.csv
1.0, 2.0
2.0, 4.0
4.0, 8.0
5.0, 10.0
7.0, 14.0
8.0, 16.0
10.0, 20.0
11.0, 22.0
13.0, 26.0
14.0, 28.0
16.0, 32.0
17.0, 34.0
19.0, 38.0
20.0, 40.0
22.0, 44.0
23.0, 46.0
25.0, 50.0
26.0, 52.0
28.0, 56.0
29.0, 58.0
train_targets_raw.csv
0
0
0
0
0
0
1
1
1
1
1
1
1
2
2
2
2
2
2
2
test_data_raw.csv
0.0, 0.0
3.0, 6.0
6.0, 12.0
9.0, 18.0
12.0, 24.0
15.0, 30.0
18.0, 36.0
21.0, 42.0
24.0, 48.0
27.0, 54.0
30.0, 60.0
test_targets_raw.csv
0
0
0
0
1
1
1
2
2
2
2
Python files
dlclsweights2.py
#################### Deep Learning (Multi-Class Classification, Supervised Learning): Implementation and Showing Biases and Weights ####################
########## How to run this code
#
# You can run this code on your MacOS Terminal (or other terminals) as follows:
#
# python3 dlclsweights2.py 200 l1l2 0.0200
# python3 dlclsweights2.py (num_epochs: number of epochs) (regl1l2: regularization) (regl1l2f: learning rate of regularization)
########## import sys
import sys
########## Argument(s)
#num_epochs = 10000
num_epochs = int(sys.argv[1])
#regl1l2 = 'None'
#regl1l2 = 'l1l2'
regl1l2 = str(sys.argv[2])
#regl1l2f = 0.001
regl1l2f = float(sys.argv[3])
#dropout_rate = 0
#dropout_rate = float(sys.argv[4])
########## import others
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score
from sklearn.metrics import r2_score
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras import Model
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Layer
from tensorflow.keras.layers import Dropout
from tensorflow.keras.optimizers import RMSprop
print(tf.__version__)
#2.3.0
########## Loading raw data (before standardization)
train_data_raw = np.loadtxt('train_data_raw.csv', dtype='float64', delimiter=',')
'''
1.0, 2.0
2.0, 4.0
4.0, 8.0
5.0, 10.0
7.0, 14.0
8.0, 16.0
10.0, 20.0
11.0, 22.0
13.0, 26.0
14.0, 28.0
16.0, 32.0
17.0, 34.0
19.0, 38.0
20.0, 40.0
22.0, 44.0
23.0, 46.0
25.0, 50.0
26.0, 52.0
28.0, 56.0
29.0, 58.0
'''
#train_targets_raw = np.loadtxt('train_targets_raw.csv', dtype='float64', delimiter=',')
train_targets_raw = np.loadtxt('train_targets_raw.csv', dtype='int', delimiter=',')
'''
0
0
0
0
0
0
1
1
1
1
1
1
1
2
2
2
2
2
2
2
'''
test_data_raw = np.loadtxt('test_data_raw.csv', dtype='float64', delimiter=',')
'''
0.0, 0.0
3.0, 6.0
6.0, 12.0
9.0, 18.0
12.0, 24.0
15.0, 30.0
18.0, 36.0
21.0, 42.0
24.0, 48.0
27.0, 54.0
30.0, 60.0
'''
#test_targets_raw = np.loadtxt('test_targets_raw.csv', dtype='float64', delimiter=',')
test_targets_raw = np.loadtxt('test_targets_raw.csv', dtype='int', delimiter=',')
'''
0
0
0
0
1
1
1
2
2
2
2
'''
########## Standardization (data/features to have average = 0, standard deviation = 1)
sc = StandardScaler()
#train_data = sc.fit_transform(train_data_raw)
train_data = train_data_raw # no standardization in this case
np.savetxt('train_data.csv', train_data, fmt ='%.8f', delimiter=',')
#
print(train_data.shape)
#(20, 2)
#
print(train_data.shape[0])
#20
#
print(train_data.shape[1])
#2
train_targets = train_targets_raw
#np.savetxt('train_targets.csv', train_targets, fmt ='%.8f', delimiter=',')
np.savetxt('train_targets.csv', train_targets, fmt ='%i', delimiter=',')
#test_data = sc.fit_transform(test_data_raw)
test_data = test_data_raw # no standardization in this case
np.savetxt('test_data.csv', test_data, fmt ='%.8f', delimiter=',')
test_targets = test_targets_raw
#np.savetxt('test_targets.csv', test_targets, fmt ='%.8f', delimiter=',')
np.savetxt('test_targets.csv', test_targets, fmt ='%i', delimiter=',')
##### Regularization
#print(regl1l2)
#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 None, l1, l2, or l1l2.')
exit()
########## Model
#all-node-connected network
'''
model = Sequential([
#Conv2D(1, (3, 3), padding='same', name='L0_conv2d', input_shape=(10, 10, 1)),
#Flatten(name='L1_flatten'),
#Dense(10, name='L2_dense', use_bias=False),
#Dense(1, name='L3_dense'),
#BatchNormalization(name='L4_bn')
#
#Dense(1, kernel_regularizer=rg, activation='relu', name='L0_dense', input_shape=(train_data.shape[1],)),
#Dense(2, kernel_regularizer=rg, activation='relu', name='L0_dense', use_bias=True, input_shape=(1,)),
Dense(1, kernel_regularizer=rg, activation='relu', name='L0_dense', use_bias=True, input_shape=(1,)),
Dropout(dropout_rate, name='L1_dropout'),
Dense(1, kernel_regularizer=rg, activation='relu', name='L2_dense', use_bias=True),
Dropout(dropout_rate, name='L3_dropout'),
Dense(1, name='L4_dense')
#Dense(1, activation='softmax', name='L4_dense')
])
'''
model = Sequential([
#Dense(1, kernel_regularizer=rg, activation='relu', name='L0_dense', use_bias=True, input_shape=(1,)),
#Use the following Dense when train_data and test_data have more-than-1 columns.
#Dense(1, kernel_regularizer=rg, activation='relu', name='L0_dense', use_bias=True, input_shape=(train_data.shape[1],)),
Dense(train_data.shape[1], kernel_regularizer=rg, activation='relu', name='L0_dense', use_bias=True, input_shape=(train_data.shape[1],)),
#Dense(1, activation='sigmoid', name='L1_dense')
Dense(1, name='L1_dense')
])
model.summary()
'''
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
L0_dense (Dense) (None, 2) 6
_________________________________________________________________
L1_dense (Dense) (None, 1) 3
=================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
_________________________________________________________________
'''
#exit()
########## Model Compiling: Regression
#model.compile(optimizer='rmsprop', loss='mse', metrics=['mean_absolute_error'])
#
########## Model Compiling: Classification (Binary Class: 0 or 1)
#model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
#
########## Model Compiling: Classification (Multi-Class)
model.compile(optimizer=RMSprop(lr=regl1l2f), loss='mse', metrics=['mae'])
########## Model Fitting and History Recording
history = model.fit( train_data,
train_targets,
validation_data=(test_data, test_targets),
epochs=num_epochs,
batch_size=1,
verbose=1) # Trains the model (in silent mode, verbose=0)
'''
...
Epoch 200/200
20/20 [==============================] - 0s 1ms/step - loss: 0.1086 - mae: 0.2616 - val_loss: 0.1553 - val_mae: 0.2887
'''
#print(history)
#<tensorflow.python.keras.callbacks.History object at 0x145d80490>
#print(history.history)
loss = history.history['loss']
val_loss = history.history['val_loss']
#mae = history.history['mean_absolute_error']
#val_mae = history.history['val_mean_absolute_error']
#acc = history.history['accuracy']
#val_acc = history.history['val_accuracy']
mae = history.history['mae']
val_mae = history.history['val_mae']
epochs = range(1, len(loss)+1)
########## Drawing figures
##### Loss
plt.plot(epochs, loss, 'r', label='Training')
plt.plot(epochs, val_loss, 'b', label='Validation')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.title('Training and validation loss')
plt.legend()
plt.savefig('Fig_1_Loss.png')
plt.show()
'''
##### Accuracy
plt.plot(epochs, acc, 'r', label='Training')
plt.plot(epochs, val_acc, 'b', label='Validation')
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.savefig('Fig_2_Accuracy.png')
plt.show()
'''
##### MAE
plt.plot(epochs, mae, 'r', label='Training')
plt.plot(epochs, val_mae, 'b', label='Validation')
plt.xlabel('epochs')
plt.ylabel('mae')
plt.title('Training and validation mae')
plt.legend()
plt.savefig('Fig_2_MAE.png')
plt.show()
########## Model Evaluation by Test Data and Test Targets
#model.evaluate(test_data, test_targets)
#1/1 [==============================] - 0s 397us/step - loss: 0.1553 - mae: 0.2887
#score = model.evaluate(test_data, test_targets)
score = model.evaluate(pd.DataFrame(test_data), test_targets, verbose=1)
print(score)
# [loss, accuracy]
#[0.15526732802391052, 0.2886613607406616]
########## Model Predictions by using Test Data
test_targets_pred = model.predict(test_data)
#test_targets_pred = model.predict_classes(test_data)
#
#predict will return the scores of the regression and predict_class will return the class of your prediction. Although it seems similar there are some differences:
#
#Imagine you are trying to predict if the picture is a dog or a cat (you have a classifier):
#
#predict will return you: 0.6 cat and 0.4 dog (for example).
#predict_class will return you cat
print(test_targets_pred)
'''
[[0.01241163]
[0.01241163]
[0.05980518]
[0.28187934]
[0.50395364]
[0.7260277 ]
[0.94810194]
[1.1701761 ]
[1.3922503 ]
[1.6143246 ]
[1.8363986 ]]
'''
#binary classification: 0 or 1 (If a predicted target is more than 0.5, then it is regarded as 1.)
#test_targets_pred = test_targets_pred > 0.5
np.savetxt('test_targets_pred.csv', test_targets_pred, fmt ='%.8f', delimiter=',')
#np.savetxt('test_targets_pred.csv', test_targets_pred, fmt ='%i', delimiter=',')
#np.savetxt('test_targets_pred.csv', np.round(test_targets_pred, decimals=0), fmt ='%i', delimiter=',')
#Confusion matrix
#print(confusion_matrix(test_targets, test_targets_pred))
########## Showing weights
print(len(model.layers))
#2
#exit()
l0 = model.layers[0]
l1 = model.layers[1]
##### Model Weights
print('##### Model Weights #####')
#model.weights has all the weights of all the layers.
print(model.weights)
'''
[<tf.Variable 'L0_dense/kernel:0' shape=(2, 2) dtype=float32, numpy=
array([[-0.00177615, -0.00999996],
[ 0.23072161, 0.00999994]], dtype=float32)>, <tf.Variable 'L0_dense/bias:0' shape=(2,) dtype=float32, numpy=array([-2.4637055, -0.5432706], dtype=float32)>, <tf.Variable 'L1_dense/kernel:0' shape=(2, 1) dtype=float32, numpy=
array([[ 0.16103989],
[-0.16875699]], dtype=float32)>, <tf.Variable 'L1_dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.01241163], dtype=float32)>]
'''
#
#
print(type(model.weights))
# <class 'list'>
#
print(len(model.weights))
# 4
print(type(model.weights[0]))
#<class 'tensorflow.python.ops.resource_variable_ops.ResourceVariable'>
#
print(model.weights[0])
'''
<tf.Variable 'L0_dense/kernel:0' shape=(2, 2) dtype=float32, numpy=
array([[-0.00177615, -0.00999996],
[ 0.23072161, 0.00999994]], dtype=float32)>
'''
#
print(model.weights[0].numpy())
'''
[[-0.00177615 -0.00999996]
[ 0.23072161 0.00999994]]
'''
print(model.weights[1])
#<tf.Variable 'L0_dense/bias:0' shape=(2,) dtype=float32, numpy=array([-2.4637055, -0.5432706], dtype=float32)>
#
print(model.weights[1].numpy())
#[-2.4637055 -0.5432706]
print(model.weights[2])
'''
<tf.Variable 'L1_dense/kernel:0' shape=(2, 1) dtype=float32, numpy=
array([[ 0.16103989],
[-0.16875699]], dtype=float32)>
'''
#
print(model.weights[2].numpy())
'''
[[ 0.16103989]
[-0.16875699]]
'''
print(model.weights[3])
#<tf.Variable 'L1_dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.01241163], dtype=float32)>
#
print(model.weights[3].numpy())
#[0.01241163]
for w in model.weights:
print('{:<25}{}'.format(w.name, w.shape))
'''
L0_dense/kernel:0 (2, 2)
L0_dense/bias:0 (2,)
L1_dense/kernel:0 (2, 1)
L1_dense/bias:0 (1,)
'''
##### Layer 0
print('##### Layer 0: Dense #####')
print(l0.weights)
'''
[<tf.Variable 'L0_dense/kernel:0' shape=(2, 2) dtype=float32, numpy=
array([[-0.00177615, -0.00999996],
[ 0.23072161, 0.00999994]], dtype=float32)>, <tf.Variable 'L0_dense/bias:0' shape=(2,) dtype=float32, numpy=array([-2.4637055, -0.5432706], dtype=float32)>]
'''
for w in l0.weights:
print('{:<25}{}'.format(w.name, w.shape))
'''
L0_dense/kernel:0 (2, 2)
L0_dense/bias:0 (2,)
'''
### kernel
print(l0.weights[0])
'''
<tf.Variable 'L0_dense/kernel:0' shape=(2, 2) dtype=float32, numpy=
array([[-0.00177615, -0.00999996],
[ 0.23072161, 0.00999994]], dtype=float32)>
'''
print(l0.weights[0].name)
#L0_dense/kernel:0
print(l0.weights[0].numpy())
'''
[[-0.00177615 -0.00999996]
[ 0.23072161 0.00999994]]
'''
### bias
print(l0.weights[1])
#<tf.Variable 'L0_dense/bias:0' shape=(2,) dtype=float32, numpy=array([-2.4637055, -0.5432706], dtype=float32)>
print(l0.weights[1].name)
#L0_dense/bias:0
print(l0.weights[1].numpy())
#[-2.4637055 -0.5432706]
##### Layer 1
#print('##### Layer 1: Dropout #####')
print('##### Layer 1: Dense #####')
print(l1.weights)
'''
[<tf.Variable 'L1_dense/kernel:0' shape=(2, 1) dtype=float32, numpy=
array([[ 0.16103989],
[-0.16875699]], dtype=float32)>, <tf.Variable 'L1_dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.01241163], dtype=float32)>]
'''
for w in l1.weights:
print('{:<25}{}'.format(w.name, w.shape))
'''
L1_dense/kernel:0 (2, 1)
L1_dense/bias:0 (1,)
'''
### kernel
print(l1.weights[0])
'''
<tf.Variable 'L1_dense/kernel:0' shape=(2, 1) dtype=float32, numpy=
array([[ 0.16103989],
[-0.16875699]], dtype=float32)>
'''
print(l1.weights[0].name)
#L1_dense/kernel:0
print(l1.weights[0].numpy())
'''
[[ 0.16103989]
[-0.16875699]]
'''
### bias
print(l1.weights[1])
#<tf.Variable 'L1_dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.01241163], dtype=float32)>
print(l1.weights[1].name)
#L1_dense/bias:0
print(l1.weights[1].numpy())
#[0.01241163]
########## Notes
##### Layer 0
#
### kernel
#
#print(l0.weights[0].name)
#L0_dense/kernel:0
#
#print(l0.weights[0].numpy())
'''
[[-0.00177615 -0.00999996]
[ 0.23072161 0.00999994]]
'''
#
### bias
#
#print(l0.weights[1].name)
#L0_dense/bias:0
#
#print(l0.weights[1].numpy())
#[-2.4637055 -0.5432706]
##### Layer 1
#
### kernel
#
#print(l1.weights[0].name)
#L1_dense/kernel:0
#
#print(l1.weights[0].numpy())
'''
[[ 0.16103989]
[-0.16875699]]
'''
#
### bias
#
#print(l1.weights[1].name)
#L1_dense/bias:0
#
#print(l1.weights[1].numpy())
#[0.01241163]
# By using these weights, we can derive the equation below:
#
# Layer 0:
# y0_0 = -2.4637055 + (-0.00177615 * x1 + 0.23072161 * x2)
# y0_1 = -0.5432706 + (-0.00999996 * x1 + 0.00999994 * x2)
#
# Layer 1:
# y = y1 = 0.01241163 + (0.16103989) * y0_0 + (-0.16875699) * y0_1
#
#
# Compare (1) calculated results by using this equation and (2) test_targets_pred: results of model.predict(test_data)
#
#test_data_raw
'''
0.0, 0.0
3.0, 6.0
6.0, 12.0
9.0, 18.0
12.0, 24.0
15.0, 30.0
18.0, 36.0
21.0, 42.0
24.0, 48.0
27.0, 54.0
30.0, 60.0
'''
#
#test_targets_pred
'''
0
0
0
0
0
1
1
1
2
2
2
'''
#
#
# (1) calculated results by using this equation
'''
For x1 = 18.0, x2 = 36.0
Layer 0:
y0_0 = -0.30038 + (0.00999996 * x1 -0.00999994 * x2)
y0_1 = -2.5460808 + (0.03288809 * x1 + 0.2756977 * x2)
y0_0 = -0.30038 + (0.00999996 * 18.0 -0.00999994 * 36.0)
y0_1 = -2.5460808 + (0.03288809 * 18.0 + 0.2756977 * 36.0)
y0_0 = -0.48037856
y0_1 = 7.97102202
Layer 1:
y = y1 = 0.12977599 + (-0.32398656) * y0_0 + (0.17396276) * y0_1
y = y1
= 0.12977599 + (-0.32398656) * y0_0 + (0.17396276) * y0_1
= 0.12977599 + (-0.32398656) * (-0.48037856) + (0.17396276) * 7.97102202
= 1.67207317777213
'''
Figures
Fig_1_Loss.png
Fig_2_MAE.png
References
No comments:
Post a Comment