from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import matplotlib.pyplot as plt
from general import generateData, train_model
import numpy as np
num_classes = 10
epochs = 10
#generate data
(x_train, y_train, x_test, y_test, input_shape) = generateData(num_classes)
#list to collect the accuracies for different learning rates
accuracies = []
#what learning rates to iterate through
learningrates = [0.001, 0.005, 0.025, 0.05, 0.1, 0.25, 0.5, 1]
for learningRate in learningrates:
accuracy = 0
#3 times for each learning rate
for j in range(3):
#Train model
model, fit_info = train_model(x_train,
y_train,
x_test,
from keras import backend as K
import matplotlib.pyplot as plt
from general import generateData, train_model
import numpy as np
import math as m
from seaborn.matrix import heatmap
import pandas as pd
from matplotlib import cm
num_classes = 10
epochs = 10
# Generate the datasets
(x_train, y_train, x_test, y_test, _) = generateData(num_classes)
neuronsToTry = [10, 25, 50, 100, 150, 250, 500, 750, 1000]
learningRatesToTry = [0.001, 0.005, 0.01, 0.05, 0.1]
#Colors for plotting
colors = ["blue","red","green","orange","purple","cyan","pink","brown"]
#List to collect the performances of the different models
modelPerformances = []
for neurons in neuronsToTry:
for lr in learningRatesToTry:
# Train model
model, fit_info = train_model(
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, GaussianNoise
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.regularizers import l2
import numpy as np
from general import generateData, train_model
num_classes = 10
#generate data
(x_train, y_train, x_test, y_test,
input_shape) = generateData(num_classes, 1, 3)
#Train convolutional model
convModel, _ = train_model(
x_train,
y_train,
x_test,
y_test,
[
GaussianNoise(0.1),
Conv2D(45, (5, 5),
activation="relu",
input_shape=input_shape,
kernel_regularizer=l2(0.001)),
MaxPooling2D(2, 2),
Conv2D(60, (5, 5), activation="relu", input_shape=input_shape),