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own_model_test.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 18 22:39:39 2021
@author: gastoncavallo
"""
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from feature_processing import classifier as mod
signal_fv = np.load("/Users/gastoncavallo/Desktop/Facultad/PI/np_dataset/signal_fv_HC.npy")
signal_fv_labels = np.load("/Users/gastoncavallo/Desktop/Facultad/PI/np_dataset/signal_fv_labels_HC.npy")
x_train, x_test, y_train, y_test = train_test_split(signal_fv, signal_fv_labels, test_size= 0.3)
def model_create1():
model1 = tf.keras.models.Sequential()
model1.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model1.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 28,
strides = 3,
activation = "relu",
padding = "same"))
model1.add(tf.keras.layers.Dropout(rate = 0.1))
model1.add(tf.keras.layers.Conv1D(filters = 32,
kernel_size = 24,
strides = 1,
activation = "relu",
padding = "same"))
model1.add(tf.keras.layers.Conv1D(filters = 32,
kernel_size = 8,
strides = 4,
activation = "relu",
padding = "same"))
model1.add(tf.keras.layers.Dropout(rate = 0.2))
model1.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model1.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 24,
strides = 2,
activation = "relu",
padding = "same"))
model1.add(tf.keras.layers.Flatten())
model1.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.002 ,
momentum = 3/10)
loss = "mse"
model1.compile(optimizer, loss, metrics = ["accuracy"])
model1.summary()
return model1
def model_create2():
model2 = tf.keras.models.Sequential()
model2.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model2.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 28,
strides = 4,
activation = "relu",
padding = "same"))
model2.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model2.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 4,
strides = 4,
activation = "relu",
padding = "same"))
model2.add(tf.keras.layers.Dropout(rate = 0.2))
model2.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 1))
model2.add(tf.keras.layers.Flatten())
model2.add(tf.keras.layers.Dense(units = 4,
activation='softmax'))
model2.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0005 ,
momentum = 2/10)
loss = "mae"
model2.compile(optimizer, loss, metrics = ["accuracy"])
model2.summary()
return model2
def model_create3():
model3 = tf.keras.models.Sequential()
model3.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model3.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 28,
strides = 4,
activation = "relu",
padding = "same"))
model3.add(tf.keras.layers.Dropout(rate = 0.2))
model3.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 24,
strides = 3,
activation = "relu",
padding = "same"))
model3.add(tf.keras.layers.Dropout(rate = 0.2))
model3.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 12,
strides = 4,
activation = "relu",
padding = "same"))
model3.add(tf.keras.layers.Dropout(rate = 0.1))
model3.add(tf.keras.layers.Flatten())
model3.add(tf.keras.layers.Dense(units = 5,
activation='softmax'))
model3.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0003 ,
momentum = 9/10)
loss = "mae"
model3.compile(optimizer, loss, metrics = ["accuracy"])
model3.summary()
return model3
def model_create4():
model4 = tf.keras.models.Sequential()
model4.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model4.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 20,
strides = 3,
activation = "relu",
padding = "same"))
model4.add(tf.keras.layers.Dropout(rate = 0.1))
model4.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 8,
strides = 3,
activation = "relu",
padding = "same"))
model4.add(tf.keras.layers.Dropout(rate = 0.3))
model4.add(tf.keras.layers.Flatten())
model4.add(tf.keras.layers.Dense(units = 6,
activation='softmax'))
model4.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.004 ,
momentum = 3/10)
loss = "mse"
model4.compile(optimizer, loss, metrics = ["accuracy"])
model4.summary()
return model4
def model_create5():
model5 = tf.keras.models.Sequential()
model5.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model5.add(tf.keras.layers.Conv1D(filters = 32,
kernel_size = 24,
strides = 2,
activation = "relu",
padding = "same"))
model5.add(tf.keras.layers.Dropout(rate = 0.2))
model5.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model5.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 28,
strides = 3,
activation = "relu",
padding = "same"))
model5.add(tf.keras.layers.Dropout(rate = 0.3))
model5.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 20,
strides = 4,
activation = "relu",
padding = "same"))
model5.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model5.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 4,
strides = 1,
activation = "relu",
padding = "same"))
model5.add(tf.keras.layers.Dropout(rate = 0.1))
model5.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model5.add(tf.keras.layers.Flatten())
model5.add(tf.keras.layers.Dense(units = 6,
activation='softmax'))
model5.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.002 ,
momentum = 2/10)
loss = "mae"
model5.compile(optimizer, loss, metrics = ["accuracy"])
model5.summary()
return model5
def model_create6():
model6 = tf.keras.models.Sequential()
model6.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model6.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 20,
strides = 3,
activation = "relu",
padding = "same"))
model6.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model6.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 12,
strides = 4,
activation = "relu",
padding = "same"))
model6.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model6.add(tf.keras.layers.Flatten())
model6.add(tf.keras.layers.Dense(units = 6,
activation='softmax'))
model6.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0004 ,
momentum = 7/10)
loss = "mse"
model6.compile(optimizer, loss, metrics = ["accuracy"])
model6.summary()
return model6
def model_create7():
model7 = tf.keras.models.Sequential()
model7.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model7.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 8,
strides = 3,
activation = "relu",
padding = "same"))
model7.add(tf.keras.layers.Dropout(rate = 0.1))
model7.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model7.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 16,
strides = 3,
activation = "relu",
padding = "same"))
model7.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model7.add(tf.keras.layers.Flatten())
model7.add(tf.keras.layers.Dense(units = 9,
activation='softmax'))
model7.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.005,
momentum = 5/10)
loss = "mse"
model7.compile(optimizer, loss, metrics = ["accuracy"])
model7.summary()
return model7
def model_create8():
model8 = tf.keras.models.Sequential()
model8.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model8.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 8,
strides = 2,
activation = "relu",
padding = "same"))
model8.add(tf.keras.layers.Dropout(rate = 0.2))
model8.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model8.add(tf.keras.layers.Conv1D(filters = 32,
kernel_size = 16,
strides = 3,
activation = "relu",
padding = "same"))
model8.add(tf.keras.layers.Dropout(rate = 0.3))
model8.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model8.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 16,
strides = 2,
activation = "relu",
padding = "same"))
model8.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model8.add(tf.keras.layers.Flatten())
model8.add(tf.keras.layers.Dense(units = 10,
activation='softmax'))
model8.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.002 ,
momentum = 8/10)
loss = "mse"
model8.compile(optimizer, loss, metrics = ["accuracy"])
model8.summary()
return model8
def model_create9():
model9 = tf.keras.models.Sequential()
model9.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model9.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 28,
strides = 3,
activation = "relu",
padding = "same"))
model9.add(tf.keras.layers.Conv1D(filters = 32,
kernel_size = 16,
strides = 2,
activation = "relu",
padding = "same"))
model9.add(tf.keras.layers.Dropout(rate = 0.1))
model9.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model9.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 4,
strides = 2,
activation = "relu",
padding = "same"))
model9.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model9.add(tf.keras.layers.Conv1D(filters = 64,
kernel_size = 28,
strides = 1,
activation = "relu",
padding = "same"))
model9.add(tf.keras.layers.Dropout(rate = 0.3))
model9.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model9.add(tf.keras.layers.Flatten())
model9.add(tf.keras.layers.Dense(units = 6,
activation='softmax'))
model9.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 5e-5,
momentum = 6/10)
loss = "mse"
model9.compile(optimizer, loss, metrics = ["accuracy"])
model9.summary()
return model9
def model_create10():
model10 = tf.keras.models.Sequential()
model10.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model10.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 12,
strides = 4,
activation = "relu",
padding = "same"))
model10.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 8,
strides = 4,
activation = "relu",
padding = "same"))
model10.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 24,
strides = 2,
activation = "relu",
padding = "same"))
model10.add(tf.keras.layers.MaxPooling1D(pool_size = 4,
strides = 2))
model10.add(tf.keras.layers.Flatten())
model10.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0001 ,
momentum = 5/10)
loss = "mse"
model10.compile(optimizer, loss, metrics = ["accuracy"])
model10.summary()
return model10
def model_create11():
model11 = tf.keras.models.Sequential()
model11.add(tf.keras.layers.Input(shape = signal_fv.shape[1:]))
model11.add(tf.keras.layers.Conv1D(filters = 16,
kernel_size = 32,
strides = 2,
activation = "relu",
padding = "same"))
model11.add(tf.keras.layers.Conv1D(filters = 8,
kernel_size = 4,
strides = 1,
activation = "relu",
padding = "same"))
model11.add(tf.keras.layers.Dropout(rate = 0.1))
model11.add(tf.keras.layers.MaxPooling1D(pool_size = 2,
strides = 1))
model11.add(tf.keras.layers.Flatten())
model11.add(tf.keras.layers.Dense(2))
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0001 ,
momentum = 4/10)
loss = "mae"
model11.compile(optimizer, loss, metrics = ["accuracy"])
model11.summary()
return model11
model = model_create11()
optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0001 ,
momentum = 4/10)
loss = "mae"
cnn_model, history = mod.model_train(model, X = signal_fv, y = signal_fv_labels, epochs = 20,
optimizer = optimizer, loss = loss,
batch_size = 32)