CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Paper • 2310.04951 • Published
id int64 1 564 | tensorflow stringclasses 52
values | pytorch nullclasses 81
values | mxnet nullclasses 66
values | paddle stringclasses 73
values |
|---|---|---|---|---|
1 | x = tf.range(12)
tf.size(x)
X = tf.reshape(x, (3, 4))
tf.zeros((2, 3, 4))
tf.ones((2, 3, 4))
tf.random.normal(shape=[3, 4])
tf.constant([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
x = tf.constant([1.0, 2, 4, 8])
y = tf.constant([2.0, 2, 2, 2])
x + y, x - y, x * y, x / y, x ** y
tf.exp(x)
X = tf.reshape(tf.range(12, dty... | null | null | x = paddle.arange(12)
x.numel()
X = paddle.reshape(x, (3, 4))
paddle.zeros((2, 3, 4))
paddle.ones((2, 3, 4))
paddle.randn((3, 4),'float32')
paddle.to_tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
x = paddle.to_tensor([1.0, 2, 4, 8])
y = paddle.to_tensor([2, 2, 2, 2])
x + y, x - y, x * y, x / y, x**y
paddle.exp(x)
... |
2 | import tensorflow as tf
X, y = tf.constant(inputs.values), tf.constant(outputs.values) | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
X, y = paddle.to_tensor(inputs.values), paddle.to_tensor(outputs.values) |
3 | import tensorflow as tf
x = tf.constant(3.0)
y = tf.constant(2.0)
print(x + y, x * y, x / y, x**y)
x = tf.range(4)
A = tf.reshape(tf.range(20), (5, 4))
tf.transpose(A)
B = tf.constant([[1, 2, 3], [2, 0, 4], [3, 4, 5]])
B == tf.transpose(B)
X = tf.reshape(tf.range(24), (2, 3, 4))
A = tf.reshape(tf.range(20, dtype=tf.flo... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
x = paddle.to_tensor([3.0])
y = paddle.to_tensor([2.0])
x + y, x * y, x / y, x**y
x = paddle.arange(4)
A = paddle.reshape(paddle.arange(20), (5, 4))
paddle.transpose(A, perm=[1, 0])
B = paddle.to_tensor([[1, 2, 3], [2, 0, 4], [3, 4, 5]])
B == paddle... |
4 | %matplotlib inline
import numpy as np
from matplotlib_inline import backend_inline
from d2l import tensorflow as d2l
def f(x):
return 3 * x ** 2 - 4 * x
def numerical_lim(f, x, h):
return (f(x + h) - f(x)) / h
h = 0.1
for i in range(5):
print(f'h={h:.5f}, numerical limit={numerical_lim(f, 1, h):.5f}')
h... | null | null | %matplotlib inline
import numpy as np
from matplotlib_inline import backend_inline
from d2l import paddle as d2l
def f(x):
return 3 * x ** 2 - 4 * x
def numerical_lim(f, x, h):
return (f(x + h) - f(x)) / h
h = 0.1
for i in range(5):
print(f'h={h:.5f}, numerical limit={numerical_lim(f, 1, h):.5f}')
h *= ... |
5 | import tensorflow as tf
x = tf.range(4, dtype=tf.float32)
x = tf.Variable(x)
with tf.GradientTape() as t:
y = 2 * tf.tensordot(x, x, axes=1)
x_grad = t.gradient(y, x)
x_grad
x_grad == 4 * x
with tf.GradientTape() as t:
y = tf.reduce_sum(x)
t.gradient(y, x)
with tf.GradientTape() as t:
y = x * x
t.gradient(y... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
x = paddle.arange(4, dtype='float32')
x = paddle.to_tensor(x, stop_gradient=False)
y = 2 * paddle.dot(x, x)
y.backward()
x.grad
x.grad == 4 * x
x.clear_gradient()
y = paddle.sum(x)
y.backward()
x.grad
x.clear_gradient()
y = x * x
paddle.sum(y).backw... |
6 | %matplotlib inline
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from d2l import tensorflow as d2l
fair_probs = tf.ones(6) / 6
tfp.distributions.Multinomial(1, fair_probs).sample()
tfp.distributions.Multinomial(10, fair_probs).sample()
counts = tfp.distributions.Multinomial(1000, fair_... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import random
import numpy as np
import paddle
fair_probs = [1.0 / 6] * 6
paddle.distribution.Multinomial(1, paddle.to_tensor(fair_probs)).sample()
counts = paddle.distribution.Multinomial(1000, paddle.to_tensor(fair_prob... |
7 | counts = tfp.distributions.Multinomial(10, fair_probs).sample(500)
cum_counts = tf.cumsum(counts, axis=0)
estimates = cum_counts / tf.reduce_sum(cum_counts, axis=1, keepdims=True)
d2l.set_figsize((6, 4.5))
for i in range(6):
d2l.plt.plot(estimates[:, i].numpy(), label=("P(die=" + str(i + 1) + ")"))
d2l.plt.axhline(... | null | null | counts = paddle.distribution.Multinomial(10, paddle.to_tensor(fair_probs)).sample((500,1))
cum_counts = counts.cumsum(axis=0)
cum_counts = cum_counts.squeeze(axis=1)
estimates = cum_counts / cum_counts.sum(axis=1, keepdim=True)
d2l.set_figsize((6, 4.5))
for i in range(6):
d2l.plt.plot(estimates[:, i],
... |
8 | %matplotlib inline
import math
import time
import numpy as np
import tensorflow as tf
from d2l import tensorflow as d2l
n = 10000
a = tf.ones(n)
b = tf.ones(n)
c = tf.Variable(tf.zeros(n))
timer = Timer()
for i in range(n):
c[i].assign(a[i] + b[i])
x = np.arange(-7, 7, 0.01)
params = [(0, 1), (0, 2), (3, 1)]
d2l.pl... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import time
import numpy as np
import paddle
n = 10000
a = paddle.ones([n])
b = paddle.ones([n])
c = paddle.zeros([n])
timer = Timer()
for i in range(n):
c[i] = a[i] + b[i]
x = np.arange(-7, 7, 0.01)
param... |
9 | %matplotlib inline
import random
import tensorflow as tf
from d2l import tensorflow as d2l
def synthetic_data(w, b, num_examples):
X = tf.zeros((num_examples, w.shape[0]))
X += tf.random.normal(shape=X.shape)
y = tf.matmul(X, tf.reshape(w, (-1, 1))) + b
y += tf.random.normal(shape=y.shape, stddev=0.01)
... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import random
import paddle
def synthetic_data(w, b, num_examples):
X = paddle.normal(0, 1, (num_examples, len(w)))
y = paddle.matmul(X, w) + b
y += paddle.normal(0, 0.01, y.shape)
return X, y.reshape((-1,... |
10 | import numpy as np
import tensorflow as tf
from d2l import tensorflow as d2l
true_w = tf.constant([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)
def load_array(data_arrays, batch_size, is_train=True):
dataset = tf.data.Dataset.from_tensor_slices(data_arrays)
if is_train:
... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import numpy as np
import paddle
true_w = paddle.to_tensor([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)
def load_array(data_arrays, batch_size, is_train=True):
dataset = paddle.io.TensorDataset(dat... |
11 | %matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
d2l.use_svg_display()
mnist_train, mnist_test = tf.keras.datasets.fashion_mnist.load_data()
len(mnist_train[0]), len(mnist_test[0])
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
figsize = (num_cols * scale, num_rows * ... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import sys
import paddle
from paddle.vision import transforms
d2l.use_svg_display()
trans = transforms.ToTensor()
mnist_train = paddle.vision.datasets.FashionMNIST(mode="train", transform=trans)
mnist_test = paddle.vision... |
12 | import tensorflow as tf
from IPython import display
from d2l import tensorflow as d2l
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs = 784
num_outputs = 10
W = tf.Variable(tf.random.normal(shape=(num_inputs, num_outputs), mean=0, stddev=0.01))
b = tf.Variable(tf.zeros(num_ou... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from IPython import display
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs = 784
num_outputs = 10
W = paddle.normal(0, 0.01, shape=(num_inputs, num_outputs))
b = paddle.zeros(shape=... |
13 | import tensorflow as tf
from d2l import tensorflow as d2l
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
net = tf.keras.models.Sequential()
net.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
weight_initializer = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.01)
net.add(tf.k... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
net = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))
def init_weights(m):
if type(m) == nn.Linear:
nn.initiali... |
14 | %matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
x = tf.Variable(tf.range(-8.0, 8.0, 0.1), dtype=tf.float32)
y = tf.nn.relu(x)
d2l.plot(x.numpy(), y.numpy(), 'x', 'relu(x)', figsize=(5, 2.5))
with tf.GradientTape() as t:
y = tf.nn.relu(x)
d2l.plot(x.numpy(), t.gradient(y, x).numpy(), 'x'... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
x = paddle.arange(-8.0, 8.0, 0.1, dtype='float32')
x.stop_gradient = False
y = paddle.nn.functional.relu(x)
d2l.plot(x.detach().numpy(), y.detach().numpy(), 'x', 'relu(x)', figsize=(5, 2.5))
y.backward(paddl... |
15 | import tensorflow as tf
from d2l import tensorflow as d2l
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = tf.Variable(tf.random.normal(shape=(num_inputs, num_hiddens), mean=0, stddev=0.01))
b1 = tf.Variable(tf.zeros(num_hiddens))
... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = paddle.randn([num_inputs, num_hiddens]) * 0.01
W1.stop_gradient = ... |
16 | import tensorflow as tf
from d2l import tensorflow as d2l
net = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(10)])
batch_size, lr, num_epochs = 256, 0.1, 10
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
trainer = tf.... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
net = nn.Sequential(nn.Flatten(),
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 10))
for layer in net:
if type(layer) == nn.Linear:
... |
17 | import math
import numpy as np
import tensorflow as tf
from d2l import tensorflow as d2l
true_w, features, poly_features, labels = [tf.constant(x, dtype=tf.float32) for x in [true_w, features, poly_features, labels]]
features[:2], poly_features[:2, :], labels[:2]
def evaluate_loss(net, data_iter, loss):
metric = d2... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import numpy as np
import paddle
from paddle import nn
true_w, features, poly_features, labels = [paddle.to_tensor(x, dtype=
paddle.float32) for x in [true_w, features, poly_features, labels]]
features[:2], poly_features[:2,... |
18 | %matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = tf.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synth... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = paddle.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter ... |
19 | import tensorflow as tf
from d2l import tensorflow as d2l
def dropout_layer(X, dropout):
assert 0 <= dropout <= 1
if dropout == 1:
return tf.zeros_like(X)
if dropout == 0:
return X
mask = tf.random.uniform(shape=tf.shape(X), minval=0, maxval=1) < 1 - dropout
return tf.cast(mask, dtyp... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import random
import paddle
from paddle import nn
warnings.filterwarnings("ignore", category=DeprecationWarning)
from d2l import paddle as d2l
def dropout_layer(X, dropout):
assert 0 <= dropout <= 1
if dropout == 1:
return paddle.zeros_like(X)
... |
20 | trainer = tf.keras.optimizers.SGD(learning_rate=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
%matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
x = tf.Variable(tf.range(-8.0, 8.0, 0.1))
with tf.GradientTape() as t:
y = tf.nn.sigmoid(x)
d2l.plot(x.numpy(), [y.num... | null | null | trainer = paddle.optimizer.SGD(learning_rate=0.5, parameters=net.parameters())
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
%matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
x = paddle.arange(start=-8.0, end=8.0, step=0.1, dtype='flo... |
21 | %matplotlib inline
import numpy as np
import pandas as pd
import tensorflow as tf
from d2l import tensorflow as d2l
n_train = train_data.shape[0]
train_features = tf.constant(all_features[:n_train].values, dtype=tf.float32)
test_features = tf.constant(all_features[n_train:].values, dtype=tf.float32)
train_labels = tf.c... | null | null | %matplotlib inline
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
warnings.filterwarnings("ignore", category=DeprecationWarning)
from d2l import paddle as d2l
n_train = train_data.shape[0]
train_features = paddle.to_tensor(all_features... |
22 | import tensorflow as tf
net = tf.keras.models.Sequential([
tf.keras.layers.Dense(256, activation=tf.nn.relu),
tf.keras.layers.Dense(10))
X = tf.random.uniform((2, 20))
net(X)
class MLP(tf.keras.Model):
def __init__(self):
super().__init__()
self.hidden = tf.keras.layers.Dense(units=256, acti... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
from paddle.nn import functional as F
net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
X = paddle.rand([2, 20])
net(X)
class MLP(nn.Layer):
def __init__(self):
super().__init__()
self.h... |
23 | import tensorflow as tf
net = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4, activation=tf.nn.relu),
tf.keras.layers.Dense(1),
])
X = tf.random.uniform((2, 4))
net(X)
net.get_weights()[1]
def block1(name):
return tf.keras.Sequential([tf.keras.layers.Flatten(), tf.keras.... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))
X = paddle.rand([2, 4])
net(X)
net.state_dict()['2.bias']
def block1():
return nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 4), nn.ReLU())
def block... |
24 | import tensorflow as tf
class CenteredLayer(tf.keras.Model):
def __init__(self):
super().__init__()
def call(self, inputs):
return inputs - tf.reduce_mean(inputs)
Y = net(tf.random.uniform((4, 8)))
tf.reduce_mean(Y)
class MyDense(tf.keras.Model):
def __init__(self, units):
super().__... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
import paddle.nn.functional as F
from paddle import nn
class CenteredLayer(nn.Layer):
def __init__(self):
super().__init__()
def forward(self, X):
return X - X.mean()
Y = net(paddle.rand([4, 8]))
Y.mean()
class MyLinear(nn.La... |
25 | import numpy as np
import tensorflow as tf
x = tf.range(4)
np.save('x-file.npy', x)
x2 = np.load('x-file.npy', allow_pickle=True)
y = tf.zeros(4)
np.save('xy-files.npy', [x, y])
x2, y2 = np.load('xy-files.npy', allow_pickle=True)
mydict = {'x': x, 'y': y}
np.save('mydict.npy', mydict)
mydict2 = np.load('mydict.npy', al... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
from paddle.nn import functional as F
x = paddle.arange(4)
paddle.save(x, 'x-file')
x2 = paddle.load('x-file')
y = paddle.zeros([4])
paddle.save([x,y], 'x-file')
x2, y2 = paddle.load('x-file')
mydict = {'x': x, 'y': y}
paddle.s... |
26 | import tensorflow as tf
tf.device('/CPU:0'), tf.device('/GPU:0'), tf.device('/GPU:1')
len(tf.config.experimental.list_physical_devices('GPU'))
def try_gpu(i=0):
if len(tf.config.experimental.list_physical_devices('GPU')) >= i + 1:
return tf.device(f'/GPU:{i}')
return tf.device('/CPU:0')
def try_all_gpus... | null | null | import paddle
from paddle import nn
paddle.device.set_device("cpu"), paddle.CUDAPlace(0), paddle.CUDAPlace(1)
paddle.device.cuda.device_count()
if paddle.device.cuda.device_count() >= i + 1:
return paddle.CUDAPlace(i)
return paddle.CPUPlace()
def try_all_gpus():
devices = [paddle.CUDAPlace(i) for i ... |
27 | import tensorflow as tf
from d2l import tensorflow as d2l
def corr2d(X, K):
h, w = K.shape
Y = tf.Variable(tf.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)))
for i in range(Y.shape[0]):
for j in range(Y.shape[1]):
Y[i, j].assign(tf.reduce_sum(
X[i: i + h, j: j + w] * K))... | null | null | import warningsfrom d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
def corr2d(X, K):
h, w = K.shape
Y = paddle.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1))
for i in range(Y.shape[0]):
for j in range(Y.shape[1]):
Y[i, j] = (X[i:i + h, j:j... |
28 | import tensorflow as tf
def comp_conv2d(conv2d, X):
X = tf.reshape(X, (1, ) + X.shape + (1, ))
Y = conv2d(X)
return tf.reshape(Y, Y.shape[1:3])
conv2d = tf.keras.layers.Conv2D(1, kernel_size=3, padding='same')
X = tf.random.uniform(shape=(8, 8))
comp_conv2d(conv2d, X).shape
conv2d = tf.keras.layers.Conv2D(... | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
def comp_conv2d(conv2d, X):
X = paddle.reshape(X, [1, 1] + X.shape)
Y = conv2d(X)
return Y.reshape(Y.shape[2:])
conv2d = nn.Conv2D(in_channels=1, out_channels=1, kernel_size=3, padding=1)
X = paddle.rand((8, 8))
co... |
29 | import tensorflow as tf
from d2l import tensorflow as d2l
def corr2d_multi_in(X, K):
return tf.reduce_sum([d2l.corr2d(x, k) for x, k in zip(X, K)], axis=0)
X = tf.constant([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])
K = tf.constant([[[0.0, 1.0], [2.0, ... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
def corr2d_multi_in(X, K):
return sum(d2l.corr2d(x, k) for x, k in zip(X, K))
X = paddle.to_tensor([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]])
K = paddle.to_t... |
30 | import tensorflow as tf
def pool2d(X, pool_size, mode='max'):
p_h, p_w = pool_size
Y = tf.Variable(tf.zeros((X.shape[0] - p_h + 1, X.shape[1] - p_w +1)))
for i in range(Y.shape[0]):
for j in range(Y.shape[1]):
if mode == 'max':
Y[i, j].assign(tf.reduce_max(X[i: i + p_h, j... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
def pool2d(X, pool_size, mode='max'):
p_h, p_w = pool_size
Y = paddle.zeros((X.shape[0] - p_h + 1, X.shape[1] - p_w + 1))
for i in range(Y.shape[0]):
for j in range(Y.shape[1]):
... |
31 | import tensorflow as tf
from d2l import tensorflow as d2l
def net():
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=6, kernel_size=5, activation='sigmoid', padding='same'),
tf.keras.layers.AvgPool2D(pool_size=2, strides=2),
tf.keras.layers.Conv2D(filters=16, kernel_size=5... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn, optimizer
net = nn.Sequential(
nn.Conv2D(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),
nn.AvgPool2D(kernel_size=2, stride=2),
nn.Conv2D(6, 16, kernel_size=5), nn.Sigmoid(),
nn.AvgPool2D(... |
32 | import tensorflow as tf
from d2l import tensorflow as d2l
def net():
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(filters=96, kernel_size=11, strides=4, activation='relu'),
tf.keras.layers.MaxPool2D(pool_size=3, strides=2),
tf.keras.layers.Conv2D(filters=256, kernel_size=5, pad... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
net = nn.Sequential(
nn.Conv2D(1, 96, kernel_size=11, stride=4, padding=1), nn.ReLU(),
nn.MaxPool2D(kernel_size=3, stride=2),
nn.Conv2D(96, 256, kernel_size=5, padding=2), nn.ReLU(),
nn.M... |
33 | import tensorflow as tf
from d2l import tensorflow as d2l
def vgg_block(num_convs, num_channels):
blk = tf.keras.models.Sequential()
for _ in range(num_convs):
blk.add(tf.keras.layers.Conv2D(num_channels,kernel_size=3, padding='same',activation='relu'))
blk.add(tf.keras.layers.MaxPool2D(pool_size=2,... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
def vgg_block(num_convs, in_channels, out_channels):
layers = []
for _ in range(num_convs):
layers.append(nn.Conv2D(in_channels, out_channels, kernel_size=3, padding=1))
layers.ap... |
34 | import tensorflow as tf
from d2l import tensorflow as d2l
def nin_block(num_channels, kernel_size, strides, padding):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(num_channels, kernel_size, strides=strides, padding=padding, activation='relu'),
tf.keras.layers.Conv2D(num_channels, kerne... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
def nin_block(in_channels, out_channels, kernel_size, strides, padding):
return nn.Sequential(
nn.Conv2D(in_channels, out_channels, kernel_size, strides, padding),
nn.ReLU(),
... |
35 | import tensorflow as tf
from d2l import tensorflow as d2l
class Inception(tf.keras.Model):
def __init__(self, c1, c2, c3, c4):
super().__init__()
self.p1_1 = tf.keras.layers.Conv2D(c1, 1, activation='relu')
self.p2_1 = tf.keras.layers.Conv2D(c2[0], 1, activation='relu')
self.p2_2 = t... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class Inception(nn.Layer):
def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
super(Inception, self).__init__(**kwargs)
self.p1_1 = nn.Conv2D(... |
36 | import tensorflow as tf
from d2l import tensorflow as d2l
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps):
inv = tf.cast(tf.math.rsqrt(moving_var + eps), X.dtype)
inv *= gamma
Y = X * inv + (beta - moving_mean * inv)
return Y
class BatchNorm(tf.keras.layers.Layer):
def __init__(self, **... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
def batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum, is_training=True):
if not is_training:
X_hat = (X - moving_mean) / (moving_var + eps) ** 0.5
else:
assert le... |
37 | import tensorflow as tf
from d2l import tensorflow as d2l
class Residual(tf.keras.Model):
def __init__(self, num_channels, use_1x1conv=False, strides=1):
super().__init__()
self.conv1 = tf.keras.layers.Conv2D(
num_channels, padding='same', kernel_size=3, strides=strides)
self.con... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
from paddle.nn import functional as F
class Residual(nn.Layer):
def __init__(self, input_channels, num_channels, use_1x1conv=False,
strides=1):
super(Residual, self).__init__(... |
38 | import tensorflow as tf
from d2l import tensorflow as d2l
class ConvBlock(tf.keras.layers.Layer):
def __init__(self, num_channels):
super(ConvBlock, self).__init__()
self.bn = tf.keras.layers.BatchNormalization()
self.relu = tf.keras.layers.ReLU()
self.conv = tf.keras.layers.Conv2D(f... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn as nn
def conv_block(input_channels, num_channels):
return nn.Sequential(
nn.BatchNorm2D(input_channels), nn.ReLU(),
nn.Conv2D(input_channels, num_channels, kernel_size=3, padding=1))
class... |
39 | %matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
T = 1000
time = tf.range(1, T + 1, dtype=tf.float32)
x = tf.sin(0.01 * time) + tf.random.normal([T], 0, 0.2)
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
tau = 4
features = tf.Variable(tf.zeros((T - tau, tau)))
for i in ran... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
T = 1000
time = paddle.arange(1, T + 1, dtype=paddle.float32)
x = paddle.sin(0.01 * time) + paddle.normal(0, 0.2, (T,))
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
... |
40 | import collections
import re
from d2l import tensorflow as d2l
def tokenize(lines, token='word'):
if token == 'word':
return [line.split() for line in lines]
elif token == 'char':
return [list(line) for line in lines]
else:
print('Error: Unknown word element type:' + token)
tokens = ... | null | null | import collections
import re
from d2l import paddle as d2l
def tokenize(lines, token='word'):
if token == 'word':
return [line.split() for line in lines]
elif token == 'char':
return [list(line) for line in lines]
else:
print('Error: Unknown word element type:' + token)
tokens = toke... |
41 | import random
import tensorflow as tf
from d2l import tensorflow as d2l
tokens = d2l.tokenize(d2l.read_time_machine())
corpus = [token for line in tokens for token in line]
vocab = d2l.Vocab(corpus)
vocab.token_freqs[:10]
def seq_data_iter_random(corpus, batch_size, num_steps):
corpus = corpus[random.randint(0, num... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import random
import paddle
tokens = d2l.tokenize(d2l.read_time_machine())
corpus = [token for line in tokens for token in line]
vocab = d2l.Vocab(corpus)
vocab.token_freqs[:10]
def seq_data_iter_random(corpus, batch_size, num_steps):
c... |
42 | import tensorflow as tf
from d2l import tensorflow as d2l
X, W_xh = tf.random.normal((3, 1), 0, 1), tf.random.normal((1, 4), 0, 1)
H, W_hh = tf.random.normal((3, 4), 0, 1), tf.random.normal((4, 4), 0, 1)
tf.matmul(X, W_xh) + tf.matmul(H, W_hh)
tf.matmul(tf.concat((X, H), 1), tf.concat((W_xh, W_hh), 0)) | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
X, W_xh = paddle.normal(0, 1, (3, 1)), paddle.normal(0, 1, (1, 4))
H, W_hh = paddle.normal(0, 1, (3, 4)), paddle.normal(0, 1, (4, 4))
paddle.matmul(X, W_xh) + paddle.matmul(H, W_hh)
paddle.matmul(paddle.concat((X, H), 1), padd... |
43 | %matplotlib inline
import math
import tensorflow as tf
from d2l import tensorflow as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
train_random_iter, vocab_random_iter = d2l.load_data_time_machine(batch_size, num_steps, use_random_iter=True)
tf.one_hot(tf.const... | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import paddle
from paddle import nn
from paddle.nn import functional as F
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
F.one_hot(paddle.to_tensor([0, 2])... |
44 | import tensorflow as tf
from d2l import tensorflow as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
num_hiddens = 256
rnn_cell = tf.keras.layers.SimpleRNNCell(num_hiddens, kernel_initializer='glorot_uniform')
rnn_layer = tf.keras.layers.RNN(rnn_cell, time_major... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
from paddle.nn import functional as F
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
num_hiddens = 256
rnn_layer = nn.SimpleRNN(len(vocab), num_hidden... |
45 | import tensorflow as tf
from d2l import tensorflow as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_params(vocab_size, num_hiddens):
num_inputs = num_outputs = vocab_size
def normal(shape):
return tf.random.normal(shape=shape,stddev=0.01... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn.functional as F
from paddle import nn
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_params(vocab_size, num_hiddens):
num_inputs = num_outputs ... |
46 | import tensorflow as tf
from d2l import tensorflow as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_lstm_params(vocab_size, num_hiddens):
num_inputs = num_outputs = vocab_size
def normal(shape):
return tf.Variable(tf.random.normal(shape=... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn.functional as Function
from paddle import nn
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_lstm_params(vocab_size, num_hiddens):
num_inputs = ... |
47 | import os
import tensorflow as tf
from d2l import tensorflow as d2l
def build_array_nmt(lines, vocab, num_steps):
lines = [vocab[l] for l in lines]
lines = [l + [vocab['<eos>']] for l in lines]
array = tf.constant([truncate_pad(l, num_steps, vocab['<pad>']) for l in lines])
valid_len = tf.reduce_sum(
... | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import os
import paddle
def build_array_nmt(lines, vocab, num_steps):
lines = [vocab[l] for l in lines]
lines = [l + [vocab['<eos>']] for l in lines]
array = paddle.to_tensor([truncate_pad(l, num_steps, vocab['<pad>']) for l in ... |
48 | null | null | null | x = paddle.arange(12)
x.numel()
X = paddle.reshape(x, (3, 4))
paddle.zeros((2, 3, 4))
paddle.ones((2, 3, 4))
paddle.randn((3, 4),'float32')
paddle.to_tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
x = paddle.to_tensor([1.0, 2, 4, 8])
y = paddle.to_tensor([2, 2, 2, 2])
x + y, x - y, x * y, x / y, x**y
paddle.exp(x)
... |
49 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
X, y = paddle.to_tensor(inputs.values), paddle.to_tensor(outputs.values) |
50 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
x = paddle.to_tensor([3.0])
y = paddle.to_tensor([2.0])
x + y, x * y, x / y, x**y
x = paddle.arange(4)
A = paddle.reshape(paddle.arange(20), (5, 4))
paddle.transpose(A, perm=[1, 0])
B = paddle.to_tensor([[1, 2, 3], [2, 0, 4], [3, 4, 5]])
B == paddle... |
51 | null | null | null | %matplotlib inline
import numpy as np
from matplotlib_inline import backend_inline
from d2l import paddle as d2l
def f(x):
return 3 * x ** 2 - 4 * x
def numerical_lim(f, x, h):
return (f(x + h) - f(x)) / h
h = 0.1
for i in range(5):
print(f'h={h:.5f}, numerical limit={numerical_lim(f, 1, h):.5f}')
h *= ... |
52 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
x = paddle.arange(4, dtype='float32')
x = paddle.to_tensor(x, stop_gradient=False)
y = 2 * paddle.dot(x, x)
x.clear_gradient()
y = paddle.sum(x)
y.backward()
x.grad
x.clear_gradient()
y = x * x
paddle.sum(y).backward()
x.grad
x.clear_gradient()
y = ... |
53 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import random
import numpy as np
import paddle
fair_probs = [1.0 / 6] * 6
paddle.distribution.Multinomial(1, paddle.to_tensor(fair_probs)).sample()
counts = paddle.distribution.Multinomial(1000, paddle.to_tensor(fair_prob... |
54 | null | null | null | counts = paddle.distribution.Multinomial(10, paddle.to_tensor(fair_probs)).sample((500,1))
cum_counts = counts.cumsum(axis=0)
cum_counts = cum_counts.squeeze(axis=1)
estimates = cum_counts / cum_counts.sum(axis=1, keepdim=True)
d2l.set_figsize((6, 4.5))
for i in range(6):
d2l.plt.plot(estimates[:, i],
... |
55 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import time
import numpy as np
import paddle
n = 10000
a = paddle.ones([n])
b = paddle.ones([n])
c = paddle.zeros([n])
timer = Timer()
for i in range(n):
c[i] = a[i] + b[i]
x = np.arange(-7, 7, 0.01)
param... |
56 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import random
import paddle
def synthetic_data(w, b, num_examples):
X = paddle.normal(0, 1, (num_examples, len(w)))
y = paddle.matmul(X, w) + b
y += paddle.normal(0, 0.01, y.shape)
return X, y.reshape((-1,... |
57 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import numpy as np
import paddle
true_w = paddle.to_tensor([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)
def load_array(data_arrays, batch_size, is_train=True):
dataset = paddle.io.TensorDataset(dat... |
58 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import sys
import paddle
from paddle.vision import transforms
d2l.use_svg_display()
trans = transforms.ToTensor()
mnist_train = paddle.vision.datasets.FashionMNIST(mode="train", transform=trans)
mnist_test = paddle.vision... |
59 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from IPython import display
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs = 784
num_outputs = 10
W = paddle.normal(0, 0.01, shape=(num_inputs, num_outputs))
b = paddle.zeros(shape=... |
60 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
net = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))
def init_weights(m):
if type(m) == nn.Linear:
nn.initiali... |
61 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
x = paddle.arange(-8.0, 8.0, 0.1, dtype='float32')
x.stop_gradient = False
y = paddle.nn.functional.relu(x)
d2l.plot(x.detach().numpy(), y.detach().numpy(), 'x', 'relu(x)', figsize=(5, 2.5))
y.backward(paddl... |
62 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = paddle.randn([num_inputs, num_hiddens]) * 0.01
W1.stop_gradient = ... |
63 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
net = nn.Sequential(nn.Flatten(),
nn.Linear(784, 256),
nn.ReLU(),
nn.Linear(256, 10))
for layer in net:
if type(layer) == nn.Linear:
... |
64 | null | null | null | import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import numpy as np
import paddle
from paddle import nn
true_w, features, poly_features, labels = [paddle.to_tensor(x, dtype=
paddle.float32) for x in [true_w, features, poly_features, labels]]
features[:2], poly_features[:2,... |
65 | null | null | null | %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = paddle.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter ... |
66 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import random
import paddle
from paddle import nn
warnings.filterwarnings("ignore", category=DeprecationWarning)
from d2l import paddle as d2l
def dropout_layer(X, dropout):
assert 0 <= dropout <= 1
if dropout == 1:
return paddle.zeros_like(X)
... |
67 | null | null | null | trainer = paddle.optimizer.SGD(learning_rate=0.5, parameters=net.parameters())
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
%matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
x = paddle.arange(start=-8.0, end=8.0, step=0.1, dtype='flo... |
68 | null | null | null | %matplotlib inline
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
warnings.filterwarnings("ignore", category=DeprecationWarning)
from d2l import paddle as d2l
n_train = train_data.shape[0]
train_features = paddle.to_tensor(all_features... |
69 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
from paddle.nn import functional as F
net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
X = paddle.rand([2, 20])
net(X)
class MLP(nn.Layer):
def __init__(self):
super().__init__()
self.h... |
70 | null | null | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))
X = paddle.rand([2, 4])
net(X)
net.state_dict()['2.bias']
def block1():
return nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 4), nn.ReLU())
def block... |
End of preview. Expand in Data Studio
license: apache-2.0
This is an exact copy of the dataset from the original github repo: https://github.com/WeixiangYAN/CodeTransOcean.git
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
CodeTransOcean, a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. CodeTransOcean consists of three novel multilingual datasets, namely, MultilingualTrans supporting translations between multiple popular programming languages, NicheTrans for translating between niche programming languages and popular ones, and LLMTrans for evaluating executability of translated code by large language models (LLMs). CodeTransOcean also includes a novel cross-framework dataset, DLTrans, for translating deep learning code across different frameworks.
Datasets
Code
The MultilingualTrans, NicheTrans, and DLTrans datasets were experimented with on CodeT5+, and the code is in the CodeT5+ file.
The LLMTrans dataset was experimented with on GPT-3.5, and the code is in the ChatGPT file.
Citation
Please cite the paper if you use the data or code from CodeTransOcean.
@article{yan2023codetransocean,
title={CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation},
author={Yan, Weixiang and Tian, Yuchen and Li, Yunzhe and Chen, Qian and Wang, Wen},
journal={arXiv preprint arXiv:2310.04951},
year={2023}
}
Contact
For questions, please feel free to reach out via email at yanweixiang.ywx@gmail.com.
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