alfadani2/adapter-mistral-v03-7b
Updated
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'C++', 'Java', 'PHP', 'VB', 'name', 'C#', 'Go', 'C', 'Python'}) and 4 missing columns ({'paddle', 'tensorflow', 'pytorch', 'mxnet'}).
This happened while the json dataset builder was generating data using
hf://datasets/WeixiangYan/CodeTransOcean/CodeTrans Datasets/MultilingualTrans/multilingual_train.json (at revision 8985d0851100f08e94735b2f2556a795ba8c69bb)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: int64
name: string
C: string
C#: string
C++: string
Go: string
Java: string
Python: string
VB: string
PHP: string
to
{'id': Value(dtype='int64', id=None), 'tensorflow': Value(dtype='string', id=None), 'pytorch': Value(dtype='string', id=None), 'mxnet': Value(dtype='string', id=None), 'paddle': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'C++', 'Java', 'PHP', 'VB', 'name', 'C#', 'Go', 'C', 'Python'}) and 4 missing columns ({'paddle', 'tensorflow', 'pytorch', 'mxnet'}).
This happened while the json dataset builder was generating data using
hf://datasets/WeixiangYan/CodeTransOcean/CodeTrans Datasets/MultilingualTrans/multilingual_train.json (at revision 8985d0851100f08e94735b2f2556a795ba8c69bb)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id int64 | tensorflow string | paddle string | mxnet null | pytorch null |
|---|---|---|---|---|
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... | 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)
... | null | null |
2 | import tensorflow as tf
X, y = tf.constant(inputs.values), tf.constant(outputs.values) | import warnings
warnings.filterwarnings(action='ignore')
import paddle
X, y = paddle.to_tensor(inputs.values), paddle.to_tensor(outputs.values) | null | null |
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... | 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... | null | null |
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... | %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 *= ... | null | null |
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... | 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... | null | null |
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_... | %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... | null | null |
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(... | 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],
... | null | null |
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... | %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... | null | null |
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)
... | %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,... | null | null |
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:
... | 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... | null | null |
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 * ... | %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... | null | null |
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... | 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=... | null | null |
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... | 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... | null | null |
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'... | %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... | null | null |
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))
... | 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 = ... | null | null |
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.... | 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:
... | null | null |
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... | 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,... | null | null |
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... | %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 ... | null | null |
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... | 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)
... | null | null |
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... | 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... | null | null |
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... | %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... | null | null |
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... | 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... | null | null |
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.... | 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... | null | null |
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().__... | 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... | null | null |
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... | 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... | null | null |
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... | 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 ... | null | null |
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))... | 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... | null | null |
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(... | 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... | null | null |
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, ... | 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... | null | null |
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... | 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]):
... | null | null |
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... | 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(... | null | null |
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... | 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... | null | null |
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,... | 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... | null | null |
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... | 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(),
... | null | null |
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... | 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(... | null | null |
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, **... | 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... | null | null |
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... | 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__(... | null | null |
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... | 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... | null | null |
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... | %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))
... | null | null |
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 = ... | 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... | null | null |
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... | 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... | null | null |
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)) | 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... | null | null |
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... | %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])... | null | null |
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... | 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... | null | null |
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... | 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 ... | null | null |
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=... | 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 = ... | null | null |
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(
... | 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 ... | null | null |
48 | 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)
... | null | null |
49 | null | import warnings
warnings.filterwarnings(action='ignore')
import paddle
X, y = paddle.to_tensor(inputs.values), paddle.to_tensor(outputs.values) | null | null |
50 | 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... | null | null |
51 | 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 *= ... | null | null |
52 | 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 = ... | null | null |
53 | 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... | null | null |
54 | 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],
... | null | null |
55 | 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... | null | null |
56 | 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,... | null | null |
57 | 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... | null | null |
58 | 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... | null | null |
59 | 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=... | null | null |
60 | 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... | null | null |
61 | 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... | null | null |
62 | 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 = ... | null | null |
63 | 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:
... | null | null |
64 | 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,... | null | null |
65 | 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 ... | null | null |
66 | 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)
... | null | null |
67 | 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... | null | null |
68 | 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... | null | null |
69 | 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... | null | null |
70 | 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... | null | null |
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