import numpy as np
from affnist_read import loadmat
from tqdm import trange
img_rows = 28
img_cols = 28
b_size = 16
nb_classes = 10
orig_input_dim = img_cols * img_rows
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
dataset = loadmat('1.mat')
y_train = dataset['affNISTdata']['label_int']
X_train = dataset['affNISTdata']['image'].transpose()
for i in trange(8):
dataset1 = loadmat(str(i + 1) + '.mat')
y_train1 = dataset1['affNISTdata']['label_int']
X_train1 = dataset1['affNISTdata']['image'].transpose()
X_train = np.vstack((X_train, X_train1))
y_train = np.hstack((y_train, y_train1))
dataset = loadmat('16.mat')
y_test = dataset['affNISTdata']['label_int']
X_test = dataset['affNISTdata']['image'].transpose()
def run_mnist(run_num, epochs=0, layers=0, neuron_count=0):
""" Run affNIST dataset and output a guess list on test and validation
sets. Dumps a pickle of the trained network state and a results file
for choosing the best parameters.
Parameters
----------
epochs : int
Number of iterations of the the traininng loop for the whole dataset
layers : int
Number of layers (not counting the input layer, but does count output
layer)
neuron_count : list
The number of neurons in each of the layers (in order), does not count
the bias term
Attributes
----------
"""
dataset = loadmat('1.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
dataset2 = loadmat('2.mat')
ans_train2 = dataset2['affNISTdata']['label_int']
train_set2 = dataset2['affNISTdata']['image'].transpose()
ans_train = np.hstack((ans_train, ans_train2))
train_set = np.vstack((train_set, train_set2))
network = Network(layers, neuron_count, train_set[1])
network.train(train_set, ans_train, epochs)
dataset = loadmat('3.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
guess_list = network.run_unseen(train_set)
print('Test Set')
test_report = network.report_results(guess_list, ans_train)
dataset = loadmat('4.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
guess_list = network.run_unseen(train_set)
print('Validation Set')
val_report = network.report_results(guess_list, ans_train)
file_name = 'finnegan/my_net_' + str(run_num) + '.pickle'
g = open(file_name, 'wb')
pickle.dump(network, g, protocol=4)
g.close()
file_name_2 = 'finnegan/my_net_report_' + str(run_num) + '.txt'
h = open(file_name_2, 'w')
details = 'Neuron Counts: ' + str(neuron_count) + '\n'
details_2 = 'Test Report: ' + test_report + '\n'
details_3 = 'Validation Report: ' + val_report + '\n'
h.write(details)
h.write(details_2)
h.write(details_3)
h.close()
return None
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(150))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# EarlyStopping(monitor='val_loss')
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
dataset = loadmat('../data3/1.mat')
y_train = dataset['affNISTdata']['label_int']
X_train = dataset['affNISTdata']['image'].transpose()
for i in trange(15):
dataset1 = loadmat('../data3/' + str(i+2) + '.mat')
y_train1 = dataset1['affNISTdata']['label_int']
X_train1 = dataset1['affNISTdata']['image'].transpose()
X_train = np.vstack((X_train, X_train1))
y_train = np.hstack((y_train, y_train1))
print("Loading orig values")
with open('train.csv', 'r') as f:
reader = csv.reader(f)
t = list(reader)
import numpy as np
from affnist_read import loadmat
from tqdm import trange
img_rows = 28
img_cols = 28
b_size = 16
nb_classes = 10
orig_input_dim = img_cols * img_rows
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
dataset = loadmat("1.mat")
y_train = dataset["affNISTdata"]["label_int"]
X_train = dataset["affNISTdata"]["image"].transpose()
for i in trange(8):
dataset1 = loadmat(str(i + 1) + ".mat")
y_train1 = dataset1["affNISTdata"]["label_int"]
X_train1 = dataset1["affNISTdata"]["image"].transpose()
X_train = np.vstack((X_train, X_train1))
y_train = np.hstack((y_train, y_train1))
dataset = loadmat("16.mat")
y_test = dataset["affNISTdata"]["label_int"]
X_test = dataset["affNISTdata"]["image"].transpose()
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(150))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# EarlyStopping(monitor='val_loss')
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
# (X_train, y_train), (X_test, y_test) = mnist.load_data()
dataset = loadmat('../data3/1.mat')
y_train = dataset['affNISTdata']['label_int']
X_train = dataset['affNISTdata']['image'].transpose()
for i in trange(15):
dataset1 = loadmat('../data3/' + str(i + 2) + '.mat')
y_train1 = dataset1['affNISTdata']['label_int']
X_train1 = dataset1['affNISTdata']['image'].transpose()
X_train = np.vstack((X_train, X_train1))
y_train = np.hstack((y_train, y_train1))
print("Loading orig values")
with open('train.csv', 'r') as f:
reader = csv.reader(f)
t = list(reader)
