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Refine tf mnist #47

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Jan 14, 2018
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Refine tf mnist #47

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655f769
refine tf mnist
chengduoZH Jan 11, 2018
059c98d
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chengduoZH Jan 12, 2018
b796d23
fix fluid/mnist.py
chengduoZH Jan 12, 2018
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248 changes: 122 additions & 126 deletions tensorflow/mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,140 +10,136 @@
import paddle.v2 as paddle
import paddle.v2.fluid as fluid

BATCH_SIZE = 128
PASS_NUM = 5
SEED = 1
DTYPE = tf.float32


def normal_scale(size, channels):
scale = (2.0 / (size**2 * channels))**0.5
return scale


# NOTE(dzhwinter) : tensorflow use Phliox random algorithm
# as normal generator, fetch out paddle random for comparization
def paddle_random_normal(shape, loc=.0, scale=1., seed=1, dtype="float32"):
program = fluid.framework.Program()
block = program.global_block()
w = block.create_var(
dtype="float32",
shape=shape,
lod_level=0,
name="param",
initializer=fluid.initializer.NormalInitializer(
loc=.0, scale=scale, seed=seed))
place = fluid.CPUPlace()
exe = fluid.Executor(place)
out = exe.run(program, fetch_list=[w])
return np.array(out[0])


train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=BATCH_SIZE)
images = tf.placeholder(DTYPE, shape=(None, 28, 28, 1))
labels = tf.placeholder(tf.int64, shape=(None, ))

# conv layer
arg = tf.convert_to_tensor(
np.transpose(
paddle_random_normal(
[20, 1, 5, 5], scale=normal_scale(5, 1), seed=SEED, dtype=DTYPE),
axes=[2, 3, 1, 0]))
conv1_weights = tf.Variable(arg)
conv1_bias = tf.Variable(tf.zeros([20]), dtype=DTYPE)
conv1 = tf.nn.conv2d(
images, conv1_weights, strides=[1, 1, 1, 1], padding="VALID")
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
pool1 = tf.nn.max_pool(
relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

arg = tf.convert_to_tensor(
np.transpose(
paddle_random_normal(
[50, 20, 5, 5], scale=normal_scale(5, 20), seed=SEED, dtype=DTYPE),
axes=[2, 3, 1, 0]))
conv2_weights = tf.Variable(arg)
conv2_bias = tf.Variable(tf.zeros([50]), dtype=DTYPE)
conv2 = tf.nn.conv2d(
pool1, conv2_weights, strides=[1, 1, 1, 1], padding="VALID")
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
pool2 = tf.nn.max_pool(
relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

pool_shape = pool2.get_shape().as_list()
hidden_dim = reduce(lambda a, b: a * b, pool_shape[1:], 1)
reshape = tf.reshape(pool2, shape=(tf.shape(pool2)[0], hidden_dim))

# fc layer
# NOTE(dzhwinter) : paddle has a NCHW data format, tensorflow has a NHWC data format
# need to convert the fc weight
paddle_weight = paddle_random_normal(
[hidden_dim, 10],
scale=normal_scale(hidden_dim, 10),
seed=SEED,
dtype=DTYPE)
new_shape = pool_shape[-1:] + pool_shape[1:-1] + [10]
paddle_weight = np.reshape(paddle_weight, new_shape)
paddle_weight = np.transpose(paddle_weight, [1, 2, 0, 3])

arg = tf.convert_to_tensor(np.reshape(paddle_weight, [hidden_dim, 10]))
fc_weights = tf.Variable(arg, dtype=DTYPE)
fc_bias = tf.Variable(tf.zeros([10]), dtype=DTYPE)
logits = tf.matmul(reshape, fc_weights) + fc_bias

# cross entropy

prediction = tf.nn.softmax(logits)

one_hot_labels = tf.one_hot(labels, depth=10)
cost = -tf.reduce_sum(tf.log(prediction) * one_hot_labels, [1])
avg_cost = tf.reduce_mean(cost)

correct = tf.equal(tf.argmax(prediction, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
g_accuracy = tf.metrics.accuracy(labels, tf.argmax(prediction, axis=1))

opt = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999)
train_op = opt.minimize(avg_cost)


def eval_test():
def parse_args():
parser = argparse.ArgumentParser("mnist model benchmark.")
parser.add_argument(
'--batch_size', type=int, default=128, help='The minibatch size.')
parser.add_argument(
'--iterations', type=int, default=35, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=5, help='The number of passes.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
args = parser.parse_args()
return args


def run_benchmark(args):
def weight_variable(dtype, shape):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=dtype)
return tf.Variable(initial)

def bias_variable(dtype, shape):
initial = tf.constant(0.1, shape=shape, dtype=dtype)
return tf.Variable(initial)

device = '/cpu:0' if args.device == 'CPU' else '/device:GPU:0'
with tf.device(device):

images = tf.placeholder(DTYPE, shape=(None, 28, 28, 1))
labels = tf.placeholder(tf.int64, shape=(None, ))

conv1_weights = weight_variable(DTYPE, [5, 5, 1, 20])
conv1_bias = bias_variable(DTYPE, [20])
conv1 = tf.nn.conv2d(
images, conv1_weights, strides=[1, 1, 1, 1], padding="VALID")
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
pool1 = tf.nn.max_pool(
relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

conv2_weights = weight_variable(DTYPE, [5, 5, 20, 50])
conv2_bias = bias_variable(DTYPE, [50])
conv2 = tf.nn.conv2d(
pool1, conv2_weights, strides=[1, 1, 1, 1], padding="VALID")
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
pool2 = tf.nn.max_pool(
relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

pool_shape = pool2.get_shape().as_list()
hidden_dim = reduce(lambda a, b: a * b, pool_shape[1:], 1)
reshape = tf.reshape(pool2, shape=(tf.shape(pool2)[0], hidden_dim))

fc_weights = weight_variable(DTYPE, [hidden_dim, 10])
fc_bias = bias_variable(DTYPE, [10])
logits = tf.matmul(reshape, fc_weights) + fc_bias
prediction = tf.nn.softmax(logits)

one_hot_labels = tf.one_hot(labels, depth=10)
cost = -tf.reduce_sum(tf.log(prediction) * one_hot_labels, [1])
avg_cost = tf.reduce_mean(cost)

correct = tf.equal(tf.argmax(prediction, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
g_accuracy = tf.metrics.accuracy(labels, tf.argmax(prediction, axis=1))

opt = tf.train.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
train_op = opt.minimize(avg_cost)
# train_op = tf.train.AdamOptimizer(1e-4).minimize(avg_cost)

train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
for batch_id, data in enumerate(test_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype("int64")
_, loss, acc, g_acc = sess.run(
[train_op, avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
return g_acc[1]


config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
for pass_id in range(PASS_NUM):
pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
paddle.dataset.mnist.test(), batch_size=args.batch_size)

def eval_test():
for batch_id, data in enumerate(test_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype("int64")
start = time.time()

_, loss, acc, g_acc = sess.run(
[train_op, avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
end = time.time()

print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
pass_end = time.time()
test_avg_acc = eval_test()
print("pass=%d, training_avg_accuracy=%f, test_avg_acc=%f, elapse=%f" %
(pass_id, g_acc[1], test_avg_acc, (pass_end - pass_start) / 1000))
return g_acc[1]
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@pkuyym pkuyym Jan 11, 2018 •
edited
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Be careful here, tf.metrics.accuracy will accumulate the acc. Please make sure to reset the accumulating variables each time doing validation. You can refer tensorflow/tensorflow#4814 (comment)

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Done. Thx!


config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
for pass_id in range(args.pass_num):
pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype(
"int64")
start = time.time()
_, loss, acc, g_acc = sess.run(
[train_op, avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
end = time.time()

print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
pass_end = time.time()
test_avg_acc = eval_test()
print(
"pass=%d, training_avg_accuracy=%f, test_avg_acc=%f, elapse=%f"
% (pass_id, g_acc[1], test_avg_acc,
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Same as above, I think better to reset the accumulating state in the begin of each epoch.

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Donw

(pass_end - pass_start) / 1000))


def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')


if __name__ == '__main__':
args = parse_args()
print_arguments(args)
run_benchmark(args)