from keras.layers import Conv2D, MaxPooling2D
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
import numpy as np
from keras.optimizers import Adam
from TestTools import plot_results, load_data
from keras import regularizers
batch_size = 128
num_classes = 10
epochs = 15
# input image dimensions
img_rows, img_cols = 16, 16
x_train, y_train, x_test, y_test = load_data(img_rows, img_cols)
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
loss_results = []
accuracy_results = []
for i in range(0, 10):
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
from keras.optimizers import Adam
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
from sklearn.metrics import confusion_matrix
import itertools
from TestTools import load_data, plot_results
if __name__ == "__main__":
loss_results = []
accuracy_results = []
img_rows, img_cols = 16, 16
for i in range(0, 1):
trainData, trainLabels, testData, testLabels = load_data(
img_rows, img_cols)
batch_size = 1
num_classes = 10
epochs = 15
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(256, )))
model.add(Dense(128, activation='sigmoid'))
model.add(Dense(128, activation='tanh'))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
