def runInference():
data, names = dataloader.loadTestData('data_part1/test/')
#rdata = np.reshape(data, (len(data), 77*71))/255
rdata = np.reshape(
data, (len(data), IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS)) / 255
output_path = "tfproject4.txt"
output = open(output_path, "w")
with tf.Session(graph=graph) as session:
s = tf.train.Saver().restore(session, "models/tf-project4/model.ckpt")
print("model loaded")
for i in range(len(data)):
ret = session.run([result],
feed_dict={
X: np.array([rdata[i]]),
is_train: 0
})
output.write("{} {}\n".format(names[i], ret[0][0]))
# cv2.imshow(names[i], data[i])
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#exit()
output.close()
print("output saved file: " + output_path)
def runLogisticRegression():
w = np.load("models/logisticRegression/w.npy")
b = np.load("models/logisticRegression/b.npy")
data, names = dataloader.loadTestData('data_part1/test/')
data = np.reshape(data, (len(data), 77 * 71)) / 255
for i in range(len(data)):
y_ = np.dot(data[i], w) + b
shot = np.argmax(y_)
print("{} {}".format(names[i], shot))
def runMlp():
import mlp
wj = np.load("models/mlp/wj.npy")
bj = np.load("models/mlp/bj.npy")
wk = np.load("models/mlp/wk.npy")
bk = np.load("models/mlp/bk.npy")
data, names = dataloader.loadTestData('data_part1/test/')
data = np.reshape(data, (len(data), 77 * 71)) / 255
for i in range(len(data)):
print("{} {}".format(names[i], mlp.inference(data[i], wj, bj, wk, bk)))
#runMlp()
#runLogisticRegression()
#runMlpTF()
dropout = tf.layers.dropout(fc, 0.4, training=is_train)
y = tf.placeholder(tf.int64, shape=(None, ))
y_one_hot = tf.one_hot(y, 10)
learning_rate = tf.placeholder(tf.float32)
out = tf.layers.dense(dropout, 10, activation=tf.nn.sigmoid)
loss = tf.reduce_mean(tf.reduce_sum((y_one_hot - out)**2))
train_op = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).minimize(loss)
result = tf.argmax(out, 1)
correct = tf.reduce_sum(tf.cast(tf.equal(result, y), tf.float32))
data, names = dataloader.loadTestData(sys.argv[1])
#rdata = np.reshape(data, (len(data), 77*71))/255
rdata = np.reshape(data,
(len(data), IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS)) / 255
output = open(sys.argv[2], "w")
with tf.Session(graph=graph) as session:
s = tf.train.Saver().restore(session, "model/model.ckpt")
for i in range(len(data)):
ret = session.run([result],
feed_dict={
X: np.array([rdata[i]]),
is_train: 0
})
output.write("{} {}\n".format(names[i], ret[0][0]))
output.close()