Alexander 和 Ava Amini 以及 Sadhana Lola 等人在 MIT 上发表了一门很棒的 ML 课程：深度学习简介。作为该课程的一部分，在 github 上发布了实验室 Lab1-Part 2....
Alexander 和 Ava Amini 以及 Sadhana Lola 等人 在 MIT 上发布了一门很棒的 ML 课程：深度学习简介 。作为该课程的一部分，在 github 上发布了实验室 Lab1-Part 2。 因此，由于我的 Google Colab 运行非常糟糕（我几个小时都无法连接到 T4），我决定将笔记本移到家里的笔记本电脑上。问题就在这里：有人能解释一下为什么同样的代码在 google colab 上可以运行，但在我本地的笔记本上却不行吗？我检查了 TF 版本，我的是 16.2，而 G-Colab 是 15.0。tf.keras.layers.Embedding 函数的签名似乎相同，但我不知道如何解决这个问题……：
### Defining the RNN Model ###
'''TODO: Add LSTM and Dense layers to define the RNN model using the Sequential API.'''
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
# Layer 1: Embedding layer to transform indices into dense vectors
# of a fixed embedding size
tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None]),
# Layer 2: LSTM with `rnn_units` number of units.
# TODO: Call the LSTM function defined above to add this layer.
LSTM(rnn_units=rnn_units),
# Layer 3: Dense (fully-connected) layer that transforms the LSTM output
# into the vocabulary size.
# TODO: Add the Dense layer.
tf.keras.layers.Dense(units=vocab_size,
activation='softmax')
])
return model
# Build a simple model with default hyperparameters. You will get the
# chance to change these later.
model = build_model(len(vocab), embedding_dim=256, rnn_units=1024, batch_size=32)
我的笔记本显示的错误如下：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[16], line 25
21 return model
23 # Build a simple model with default hyperparameters. You will get the
24 # chance to change these later.
---> 25 model = build_model(len(vocab), embedding_dim=256, rnn_units=1024, batch_size=32)
Cell In[16], line 8, in build_model(vocab_size, embedding_dim, rnn_units, batch_size)
4 def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
5 model = tf.keras.Sequential([
6 # Layer 1: Embedding layer to transform indices into dense vectors
7 # of a fixed embedding size
----> 8 tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None]),
9
10 # Layer 2: LSTM with `rnn_units` number of units.
11 # TODO: Call the LSTM function defined above to add this layer.
12 LSTM(rnn_units=rnn_units),
13
14 # Layer 3: Dense (fully-connected) layer that transforms the LSTM output
15 # into the vocabulary size.
16 # TODO: Add the Dense layer.
17 tf.keras.layers.Dense(units=vocab_size,
18 activation='softmax')
19 ])
21 return model
File ~/miniconda3/envs/gpt_env/lib/python3.10/site-packages/keras/src/layers/core/embedding.py:93, in Embedding.__init__(self, input_dim, output_dim, embeddings_initializer, embeddings_regularizer, embeddings_constraint, mask_zero, weights, lora_rank, **kwargs)
89 if input_length is not None:
90 warnings.warn(
91 "Argument `input_length` is deprecated. Just remove it."
92 )
---> 93 super().__init__(**kwargs)
94 self.input_dim = input_dim
95 self.output_dim = output_dim
File ~/miniconda3/envs/gpt_env/lib/python3.10/site-packages/keras/src/layers/layer.py:266, in Layer.__init__(self, activity_regularizer, trainable, dtype, autocast, name, **kwargs)
264 self._input_shape_arg = input_shape_arg
265 if kwargs:
--> 266 raise ValueError(
267 "Unrecognized keyword arguments "
268 f"passed to {self.__class__.__name__}: {kwargs}"
269 )
271 self._path = None # Will be determined in `build_wrapper`
272 self.built = False
ValueError: Unrecognized keyword arguments passed to Embedding: {'batch_input_shape': [32, None]}
当我尝试训练我的模型时，出现错误 — \'ValueError: 无法将'8'转换为形状\'，而我的 X_train 的形状是 (5347,28)。import tensorflow as tffrom tensorflow.keras im...
当我尝试训练我的模型时，出现错误-'ValueError：无法将'8'转换为形状'，而我的X_train的形状是（5347,28）。
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(tf.keras.Input(shape=(X_train.shape[1])))
model.add(Dense(units=25,activation='relu'))
model.add(Dense(units=15 , activation='relu'))
model.add(Dense(units=1 , activation='linear'))
model.summary()
loss_fun = tf.keras.losses.MSE
model.compile(loss = loss_fun,
optimizer = 'adam',
metrics=['accuracy']
)
history = model.fit(X_train,y_train,epochs=25)
我尝试了不同的方法，但仍然出现一些不同的错误。
我尝试在我的应用程序中创建 AddMembersForm 到 Group。'GroupDetailView' 对象没有属性 'object'class GroupDetailView(DetailView): model = Group context_object_name = 'group'
我尝试在我的应用程序中创建 AddMembersForm 到 Group。'GroupDetailView ' 对象没有属性 'object'
class GroupDetailView(DetailView):
model = Group
context_object_name = 'group'
template_name = 'rooms/group_detail.html'
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['form'] = AddGroupMemberForm(group_id=self.object.id)
context['members'] = GroupMembers.objects.filter(group=self.object) #Download all members
return context
def post(self, request, *args, **kwargs):
form = AddGroupMemberForm(request.POST, group_id=self.object.id)
if form.is_valid():
new_member = form.save(commit=False)
new_member.group = self.get_object()
new_member.save()
return redirect(self.get_object().get_absolute_url())
else:
return self.render_to_response(self.get_context_data(form=form))
-----------------------------------------------------------------------------------
class Group(models.Model):
author = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='creator_group', on_delete=models.CASCADE)
name = models.TextField(max_length=50)
description = models.TextField(max_length=500)
def __str__(self):
return self.name
def get_absolute_url(self):
return reverse('group_detail', kwargs={'pk': self.pk})
class GroupMembers(models.Model):
group = models.ForeignKey(Group, on_delete=models.CASCADE, related_name='membership')
user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, related_name='group_membership')
role = models.TextField(max_length=30, default='Członek grupy')
time_release = models.DateTimeField(default=timezone.now)
def __str__(self):
return f'{self.user.username} należy do {self.group.name}'
我想将用户添加到我的群组（群组创始人将有此选项）。
您能告诉我您的训练数据集是否已正确转换为张量或 numpy 数组吗？如果没有看到 X_train 和 y_train 数据及其 dtypes，就很难判断。
在以下像你试图得到的那样 self.object.id ，你的 self 是 GroupDetailView .
form = AddGroupMemberForm(request.POST, group_id=self.object.id)
然后您可以添加以下内容。如果你发送的是 group_id /add/member/ 这样的 URL
def post(self, request, group_id,**kwargs):
form = AddGroupMemberForm(request.POST, group_id=group_id)
我使用了僧伽罗语英语数据集并训练它来翻译僧伽罗语英语 java 相关问题。它提供了良好的训练准确性，但问题是保存后无法加载并使用它进行开发...
我使用了僧伽罗英语数据集并训练它来翻译僧伽罗英语 java 相关问题。它提供了良好的训练准确性，但问题是保存后无法加载并使用它开发翻译 api。我将此模型保存为 h5 格式并尝试加载它给出
import os
import shutil
import subprocess
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
import plotly.express as px
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from keras import layers
!pip install colorama
from colorama import Fore, Style
from IPython.core.display import HTML
warnings.filterwarnings("ignore")
easy_dataset_user = "test"
easy_dataset = "language-translation-englishfrench"
data_dir = Path("data")
from google.colab import drive
drive.mount('/content/drive')
if not ON_KAGGLE:
download_dataset_from_kaggle(easy_dataset_user, easy_dataset, data_dir)
easy_dataset_path = "/content/drive/MyDrive/java_sinhala_vocabulary.csv"
else:
easy_dataset_path = Path(
"/content/drive/MyDrive/java_sinhala_vocabulary.csv"
)
easy_dataset = pd.read_csv(easy_dataset_path, encoding="utf-8", engine="pyarrow")
easy_dataset = easy_dataset.sample(len(easy_dataset), random_state=42)
easy_dataset.head()
easy_dataset.info()
easy_dataset["English Words in Sentence"] = (
easy_dataset["english"].str.split().apply(len)
)
easy_dataset["French Words in Sentence"] = (
easy_dataset["sinhala"].str.split().apply(len)
)
fig = px.histogram(
easy_dataset,
x=["English Words in Sentence", "French Words in Sentence"],
color_discrete_sequence=["#3f384a", "#e04c5f"],
labels={"variable": "Variable", "value": "Words in Sentence"},
marginal="box",
barmode="group",
height=540,
width=840,
title="Easy Dataset - Words in Sentence",
)
fig.update_layout(
font_color=FONT_COLOR,
title_font_size=18,
plot_bgcolor=BACKGROUND_COLOR,
paper_bgcolor=BACKGROUND_COLOR,
bargap=0.2,
bargroupgap=0.1,
legend=dict(orientation="h", yanchor="bottom", xanchor="right", y=1.02, x=1),
yaxis_title="Count",
)
fig.show()
sentences_en = easy_dataset["english"].to_numpy()
sentences_fr = easy_dataset["sinhala"].to_numpy()
valid_fraction = 0.1
valid_len = int(valid_fraction * len(easy_dataset))
sentences_en_train = sentences_en[:-valid_len]
sentences_fr_train = sentences_fr[:-valid_len]
sentences_en_valid = sentences_en[-valid_len:]
sentences_fr_valid = sentences_fr[-valid_len:]
def prepare_input_and_target(sentences_en, sentences_fr):
"""Return data in the format: `((encoder_input, decoder_input), target)`"""
return (sentences_en, b"startofseq " + sentences_fr), sentences_fr + b" endofseq"
def from_sentences_dataset(
sentences_en,
sentences_fr,
batch_size=32,
cache=True,
shuffle=False,
shuffle_buffer_size=10_000,
seed=None,
):
dataset = tf.data.Dataset.from_tensor_slices((sentences_en, sentences_fr))
dataset = dataset.map(prepare_input_and_target, num_parallel_calls=tf.data.AUTOTUNE)
if cache:
dataset = dataset.cache()
if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size, seed=seed)
return dataset.batch(batch_size)
benchmark_ds = from_sentences_dataset(sentences_en_train, sentences_fr_train)
benchmark_ds = benchmark_ds.prefetch(tf.data.AUTOTUNE)
bench_results = tfds.benchmark(benchmark_ds, batch_size=32)
example_ds = from_sentences_dataset(
sentences_en_train, sentences_fr_train, batch_size=4
)
list(example_ds.take(1))[0]
example_ds.cardinality() # Number of batches per epoch.
class ColoramaVerbose(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
print(
f"{CLR}Epoch: {RED}{epoch + 1:02d}{CLR} -",
f"{CLR}loss: {RED}{logs['loss']:.5f}{CLR} -",
f"{CLR}accuracy: {RED}{logs['accuracy']:.5f}{CLR} -",
f"{CLR}val_loss: {RED}{logs['val_loss']:.5f}{CLR} -",
f"{CLR}val_accuracy: {RED}{logs['val_accuracy']:.5f}",
)
def adapt_compile_and_fit(
model,
train_dataset,
valid_dataset,
n_epochs=50,
n_patience=5,
init_lr=0.001,
lr_decay_rate=0.1,
colorama_verbose=False,
):
model.vectorization_en.adapt(
train_dataset.map(
lambda sentences, target: sentences[0], # English sentences.
num_parallel_calls=tf.data.AUTOTUNE,
)
)
model.vectorization_fr.adapt(
train_dataset.map(
lambda sentences, target: sentences[1] + b" endofseq", # French sentences.
num_parallel_calls=tf.data.AUTOTUNE,
)
)
train_dataset_prepared = train_dataset.map(
lambda sentences, target: (sentences, model.vectorization_fr(target)),
num_parallel_calls=tf.data.AUTOTUNE,
).prefetch(tf.data.AUTOTUNE)
valid_dataset_prepared = valid_dataset.map(
lambda sentences, target: (sentences, model.vectorization_fr(target)),
num_parallel_calls=tf.data.AUTOTUNE,
).prefetch(tf.data.AUTOTUNE)
early_stopping_cb = keras.callbacks.EarlyStopping(
monitor="val_accuracy", patience=n_patience, restore_best_weights=True
)
# The line below doesn't work with multi-file interleaving.
# n_decay_steps = n_epochs * train_dataset_prepared.cardinality().numpy()
# Less elegant solution.
n_decay_steps = n_epochs * len(list(train_dataset_prepared))
scheduled_lr = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=init_lr,
decay_steps=n_decay_steps,
decay_rate=lr_decay_rate,
)
model_callbacks = [early_stopping_cb]
verbose_level = 1
if colorama_verbose:
model_callbacks.append(ColoramaVerbose())
verbose_level = 0
model.compile(
loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.RMSprop(learning_rate=scheduled_lr),
metrics=["accuracy"],
)
return model.fit(
train_dataset_prepared,
epochs=n_epochs,
validation_data=valid_dataset_prepared,
callbacks=model_callbacks,
verbose=verbose_level,
)
def translate(model, sentence_en):
translation = ""
for word_idx in range(model.max_sentence_len):
X_encoder = np.array([sentence_en])
X_decoder = np.array(["startofseq " + translation])
# Last token's probas.
y_proba = model.predict((X_encoder, X_decoder), verbose=0)[0, word_idx]
predicted_word_id = np.argmax(y_proba)
predicted_word = model.vectorization_fr.get_vocabulary()[predicted_word_id]
if predicted_word == "endofseq":
break
translation += " " + predicted_word
return translation.strip()
class BidirectionalEncoderDecoderWithAttention(keras.Model):
def __init__(
self,
vocabulary_size=5000,
max_sentence_len=50,
embedding_size=256,
n_units_lstm=512,
**kwargs,
):
super().__init__(**kwargs)
self.max_sentence_len = max_sentence_len
self.vectorization_en = layers.TextVectorization(
vocabulary_size, output_sequence_length=max_sentence_len
)
self.vectorization_fr = layers.TextVectorization(
vocabulary_size, output_sequence_length=max_sentence_len
)
self.encoder_embedding = layers.Embedding(
vocabulary_size, embedding_size, mask_zero=True
)
self.decoder_embedding = layers.Embedding(
vocabulary_size, embedding_size, mask_zero=True
)
self.encoder = layers.Bidirectional(
layers.LSTM(n_units_lstm // 2, return_sequences=True, return_state=True)
)
self.decoder = layers.LSTM(n_units_lstm, return_sequences=True)
self.attention = layers.Attention()
self.output_layer = layers.Dense(vocabulary_size, activation="softmax")
def call(self, inputs):
encoder_inputs, decoder_inputs = inputs
encoder_input_ids = self.vectorization_en(encoder_inputs)
decoder_input_ids = self.vectorization_fr(decoder_inputs)
encoder_embeddings = self.encoder_embedding(encoder_input_ids)
decoder_embeddings = self.decoder_embedding(decoder_input_ids)
# The final hidden state of the encoder, representing the entire
# input sequence, is used to initialize the decoder.
encoder_output, *encoder_state = self.encoder(encoder_embeddings)
encoder_state = [
tf.concat(encoder_state[0::2], axis=-1), # Short-term state (0 & 2).
tf.concat(encoder_state[1::2], axis=-1), # Long-term state (1 & 3).
]
decoder_output = self.decoder(decoder_embeddings, initial_state=encoder_state)
attention_output = self.attention([decoder_output, encoder_output])
return self.output_layer(attention_output)
K.clear_session() # Resets all state generated by Keras.
tf.random.set_seed(42) # Ensure reproducibility on CPU.
easy_train_ds = from_sentences_dataset(
sentences_en_train, sentences_fr_train, shuffle=True, seed=42
)
easy_valid_ds = from_sentences_dataset(sentences_en_valid, sentences_fr_valid)
bidirect_encoder_decoder = BidirectionalEncoderDecoderWithAttention(max_sentence_len=15)
bidirect_history = adapt_compile_and_fit(
bidirect_encoder_decoder,
easy_train_ds,
easy_valid_ds,
init_lr=0.01,
lr_decay_rate=0.01,
colorama_verbose=True,
)
fig = px.line(
bidirect_history.history,
markers=True,
height=540,
width=840,
symbol="variable",
labels={"variable": "Variable", "value": "Value", "index": "Epoch"},
title="Easy Dataset - Encoder-Decoder RNN Training Process",
color_discrete_sequence=px.colors.diverging.balance_r,
)
fig.update_layout(
font_color=FONT_COLOR,
title_font_size=18,
plot_bgcolor=BACKGROUND_COLOR,
paper_bgcolor=BACKGROUND_COLOR,
)
fig.show()
translation1 = translate(bidirect_encoder_decoder, "Hello, how are you?")
translation2 = translate(bidirect_encoder_decoder, "This is a test sentence")
translation3 = translate(bidirect_encoder_decoder, "you will receive a confirmation code after completing the registration.")
print(CLR + "Actual Possible Translations:")
print(BLUE + "Hello, how are you?".ljust(25), RED + "-> ", BLUE + "හෙලෝ, ඔයාට කෙසේද?")
print(
BLUE + "This is a test sentence".ljust(25),
RED + "-> ",
BLUE + "මෙය පරීක්ෂණ වාක්යයකි.",
)
print(
BLUE + "you will receive a confirmation code after completing the registration".ljust(25),
RED + "-> ",
BLUE + "ලියාපදිංචිය සම්පූර්ණ කලාට පස්සෙ ඔයාට තහවුරු කිරීමේ කේතයක් හම්බවේවි",
)
print()
print(CLR + "Model Translations:")
print(BLUE + "Hello, how are you?".ljust(25), RED + "-> ", BLUE + translation1)
print(BLUE + "This is a test sentence".ljust(25), RED + "-> ", BLUE + translation2)
print(BLUE + "you will receive a confirmation code after completing the registration".ljust(25), RED + "-> ", BLUE + translation3)
PS D:app> python model.py
2024-07-21 00:08:39.758670: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-07-21 00:08:40.515736: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Traceback (most recent call last):
File "D:app\model.py", line 15, in <module>
model = tf.keras.models.load_model(model_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
保存此模型后，请帮助我构建 api。我想使用这个保存的模型构建 api 来获取给定英文句子的含义。我们需要以哪种格式保存此模型？
这就是我尝试开发 API 的方式。
import numpy as np
import keras
from keras import ops
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Layer
from fastapi import FastAPI, HTTPException
import tensorflow as tf
import numpy as np
app = FastAPI()
# Load the model
model_path = "english_sinhala_transformer_model.h5"
model = tf.keras.models.load_model(model_path)
def translate(sentence_en):
translation = ""
for word_idx in range(model.max_sentence_len):
X_encoder = np.array([sentence_en])
X_decoder = np.array(["startofseq " + translation])
y_proba = model.predict((X_encoder, X_decoder), verbose=0)[0, word_idx]
predicted_word_id = np.argmax(y_proba)
predicted_word = model.vectorization_fr.get_vocabulary()[predicted_word_id]
if predicted_word == "endofseq":
break
translation += " " + predicted_word
return translation.strip()
@app.get("/translate")
def get_translation(sentence: str):
try:
translation = translate(sentence)
return {"translation": translation}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
() 32 # 使用自定义层加载模型 33 with custom_object_scope({'PositionalEmbedding': PositionalEmbedding}): ---> 34 model = load_model('/content/drive/My Drive/trained_model.h5') 35 36 # 使用模型进行预测TypeError Traceback（最近一次调用最后一次）在
/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py in from_config(cls, config) 868 return cls(**config) 869 except Exception as e: --> 870 raise TypeError( 871 f \'使用 \' 反序列化类“{cls。__name__}”时出错 872 f \'config={config}.\n\n遇到异常：{e} \' TypeError：使用 config={'name': 'positional_embedding', 'trainable': True, 'dtype': 'float32', 'output_dim': 256, 'sequence_length': 20, 'input_dim': 15000} 反序列化类“PositionalEmbedding”时出错。
