fishcharlie

I just learned that the Eve Energy smart plugs transmit energy consumption information via Matter. I didn't think energy consumption information was supported in Matter yet, but it is.

This makes them incredible to use with the Home Assistant Energy dashboard.

Even tho I was hesitant for a while, I took the leap to using the Matter beta Home Assistant integration and no issues so far.

Super happy to announce the release of multiple account support in Echo v1.4! Easily change between Lemmy accounts (even across multiple instances/servers) in Echo without having to logout of your existing account.

The full release notes are listed below.

- Multiple Account support!
    - Do you have multiple Lemmy accounts? Maybe across multiple instances? Well now you can sign into all of them in Echo without having to logout of your existing account.
    - Requires Echo+ subscription.
- Fixes issue where community list would flash results when opening.
- Adds loading indicator to Explore page after searching.
- Lemmy 0.19.6 support & improvements.
- Fixes issue where in rare cases deleted/removed communities would show in the community list.
- Vast performance improvements.
- More behind the scenes improvements than we can count.

It seems like running a pictrs server is optional when running Lemmy. I'm trying to figure out if a given instance supports pictrs.

I see in the documentation for pictrs, there is a GET /healthz endpoint. However when I try to access https://lemmy.ml/pictrs/healthz for example it gives me a 404. Even tho I know that Lemmy.ml has a pictrs server.

What is the best way to determine if a Lemmy server has pictrs?

cross-posted from: https://eventfrontier.com/post/177049

I keep getting an error ValueError: perm should have the same length as rank(x): 3 != 2 when trying to convert my model using coremltools.

From my understanding the most common case for this is when your input shape that you pass into coremltools doesn't match your model input shape. However, as far as I can tell in my code it does match. I also added an input layer, and that didn't help either.

I have put a lot of effort into reducing my code as much as possible while still giving a minimal complete verifiable example. However, I'm aware that the code is still a lot. Starting at line 60 of my code is where I create my model, and train it.

I'm running this on Ubuntu, with NVIDIA setup with Docker.

Any ideas what I'm doing wrong?

from typing import TypedDict, Optional, List
import tensorflow as tf
import json
from tensorflow.keras.optimizers import Adam
import numpy as np
from sklearn.utils import resample
import keras
import coremltools as ct

# Simple tokenizer function
word_index = {}
index = 1
def tokenize(text: str) -> list:
    global word_index
    global index
    words = text.lower().split()
    sequences = []
    for word in words:
        if word not in word_index:
            word_index[word] = index
            index += 1
        sequences.append(word_index[word])
    return sequences

def detokenize(sequence: list) -> str:
    global word_index
    # Filter sequence to remove all 0s
    sequence = [int(index) for index in sequence if index != 0.0]
    words = [word for word, index in word_index.items() if index in sequence]
    return ' '.join(words)

# Pad sequences to the same length
def pad_sequences(sequences: list, max_len: int) -> list:
    padded_sequences = []
    for seq in sequences:
        if len(seq) > max_len:
            padded_sequences.append(seq[:max_len])
        else:
            padded_sequences.append(seq + [0] * (max_len - len(seq)))
    return padded_sequences

class PreprocessDataResult(TypedDict):
    inputs: tf.Tensor
    labels: tf.Tensor
    max_len: int

def preprocess_data(texts: List[str], labels: List[int], max_len: Optional[int] = None) -> PreprocessDataResult:
    tokenized_texts = [tokenize(text) for text in texts]
    if max_len is None:
        max_len = max(len(seq) for seq in tokenized_texts)
    padded_texts = pad_sequences(tokenized_texts, max_len)

    return PreprocessDataResult({
        'inputs': tf.convert_to_tensor(np.array(padded_texts, dtype=np.float32)),
        'labels': tf.convert_to_tensor(np.array(labels, dtype=np.int32)),
        'max_len': max_len
    })

# Define your model architecture
def create_model(input_shape: int) -> keras.models.Sequential:
    model = keras.models.Sequential()

    model.add(keras.layers.Input(shape=(input_shape,), dtype='int32', name='embedding_input'))
    model.add(keras.layers.Embedding(input_dim=10000, output_dim=128)) # `input_dim` represents the size of the vocabulary (i.e. the number of unique words in the dataset).
    model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=64, return_sequences=True)))
    model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=32)))
    model.add(keras.layers.Dense(units=64, activation='relu'))
    model.add(keras.layers.Dropout(rate=0.5))
    model.add(keras.layers.Dense(units=1, activation='sigmoid')) # Output layer, binary classification (meaning it outputs a 0 or 1, false or true). The sigmoid function outputs a value between 0 and 1, which can be interpreted as a probability.

    model.compile(
        optimizer=Adam(),
        loss='binary_crossentropy',
        metrics=['accuracy']
    )

    return model

# Train the model
def train_model(
    model: tf.keras.models.Sequential,
    train_data: tf.Tensor,
    train_labels: tf.Tensor,
    epochs: int,
    batch_size: int
) -> tf.keras.callbacks.History:
    return model.fit(
        train_data,
        train_labels,
        epochs=epochs,
        batch_size=batch_size,
        callbacks=[
            keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=5),
            keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1),
            # When downgrading from TensorFlow 2.18.0 to 2.12.0 I had to change this from `./best_model.keras` to `./best_model.tf`
            keras.callbacks.ModelCheckpoint(filepath='./best_model.tf', monitor='val_accuracy', save_best_only=True)
        ]
    )

# Example usage
if __name__ == "__main__":
    # Check available devices
    print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

    with tf.device('/GPU:0'):
        print("Loading data...")
        data = (["I love this!", "I hate this!"], [0, 1])
        rawTexts = data[0]
        rawLabels = data[1]

        # Preprocess data
        processedData = preprocess_data(rawTexts, rawLabels)
        inputs = processedData['inputs']
        labels = processedData['labels']
        max_len = processedData['max_len']

        print("Data loaded. Max length: ", max_len)

        # Save word_index to a file
        with open('./word_index.json', 'w') as file:
            json.dump(word_index, file)

        model = create_model(max_len)

        print('Training model...')
        train_model(model, inputs, labels, epochs=1, batch_size=32)
        print('Model trained.')

        # When downgrading from TensorFlow 2.18.0 to 2.12.0 I had to change this from `./best_model.keras` to `./best_model.tf`
        model.load_weights('./best_model.tf')
        print('Best model weights loaded.')

        # Save model
        # I think that .h5 extension allows for converting to CoreML, whereas .keras file extension does not
        model.save('./toxic_comment_analysis_model.h5')
        print('Model saved.')

        my_saved_model = tf.keras.models.load_model('./toxic_comment_analysis_model.h5')
        print('Model loaded.')

        print("Making prediction...")
        test_string = "Thank you. I really appreciate it."
        tokenized_string = tokenize(test_string)
        padded_texts = pad_sequences([tokenized_string], max_len)
        tensor = tf.convert_to_tensor(np.array(padded_texts, dtype=np.float32))
        predictions = my_saved_model.predict(tensor)
        print(predictions)
        print("Prediction made.")


        # Convert the Keras model to Core ML
        coreml_model = ct.convert(
            my_saved_model,
            inputs=[ct.TensorType(shape=(max_len,), name="embedding_input", dtype=np.int32)],
            source="tensorflow"
        )

        # Save the Core ML model
        coreml_model.save('toxic_comment_analysis_model.mlmodel')
        print("Model successfully converted to Core ML format.")

Code including Dockerfile & start script as GitHub Gist: https://gist.github.com/fishcharlie/af74d767a3ba1ffbf18cbc6d6a131089

I keep getting an error ValueError: perm should have the same length as rank(x): 3 != 2 when trying to convert my model using coremltools.

From my understanding the most common case for this is when your input shape that you pass into coremltools doesn't match your model input shape. However, as far as I can tell in my code it does match. I also added an input layer, and that didn't help either.

I have put a lot of effort into reducing my code as much as possible while still giving a minimal complete verifiable example. However, I'm aware that the code is still a lot. Starting at line 60 of my code is where I create my model, and train it.

I'm running this on Ubuntu, with NVIDIA setup with Docker.

Any ideas what I'm doing wrong?

from typing import TypedDict, Optional, List
import tensorflow as tf
import json
from tensorflow.keras.optimizers import Adam
import numpy as np
from sklearn.utils import resample
import keras
import coremltools as ct

# Simple tokenizer function
word_index = {}
index = 1
def tokenize(text: str) -> list:
    global word_index
    global index
    words = text.lower().split()
    sequences = []
    for word in words:
        if word not in word_index:
            word_index[word] = index
            index += 1
        sequences.append(word_index[word])
    return sequences

def detokenize(sequence: list) -> str:
    global word_index
    # Filter sequence to remove all 0s
    sequence = [int(index) for index in sequence if index != 0.0]
    words = [word for word, index in word_index.items() if index in sequence]
    return ' '.join(words)

# Pad sequences to the same length
def pad_sequences(sequences: list, max_len: int) -> list:
    padded_sequences = []
    for seq in sequences:
        if len(seq) > max_len:
            padded_sequences.append(seq[:max_len])
        else:
            padded_sequences.append(seq + [0] * (max_len - len(seq)))
    return padded_sequences

class PreprocessDataResult(TypedDict):
    inputs: tf.Tensor
    labels: tf.Tensor
    max_len: int

def preprocess_data(texts: List[str], labels: List[int], max_len: Optional[int] = None) -> PreprocessDataResult:
    tokenized_texts = [tokenize(text) for text in texts]
    if max_len is None:
        max_len = max(len(seq) for seq in tokenized_texts)
    padded_texts = pad_sequences(tokenized_texts, max_len)

    return PreprocessDataResult({
        'inputs': tf.convert_to_tensor(np.array(padded_texts, dtype=np.float32)),
        'labels': tf.convert_to_tensor(np.array(labels, dtype=np.int32)),
        'max_len': max_len
    })

# Define your model architecture
def create_model(input_shape: int) -> keras.models.Sequential:
    model = keras.models.Sequential()

    model.add(keras.layers.Input(shape=(input_shape,), dtype='int32', name='embedding_input'))
    model.add(keras.layers.Embedding(input_dim=10000, output_dim=128)) # `input_dim` represents the size of the vocabulary (i.e. the number of unique words in the dataset).
    model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=64, return_sequences=True)))
    model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=32)))
    model.add(keras.layers.Dense(units=64, activation='relu'))
    model.add(keras.layers.Dropout(rate=0.5))
    model.add(keras.layers.Dense(units=1, activation='sigmoid')) # Output layer, binary classification (meaning it outputs a 0 or 1, false or true). The sigmoid function outputs a value between 0 and 1, which can be interpreted as a probability.

    model.compile(
        optimizer=Adam(),
        loss='binary_crossentropy',
        metrics=['accuracy']
    )

    return model

# Train the model
def train_model(
    model: tf.keras.models.Sequential,
    train_data: tf.Tensor,
    train_labels: tf.Tensor,
    epochs: int,
    batch_size: int
) -> tf.keras.callbacks.History:
    return model.fit(
        train_data,
        train_labels,
        epochs=epochs,
        batch_size=batch_size,
        callbacks=[
            keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=5),
            keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1),
            # When downgrading from TensorFlow 2.18.0 to 2.12.0 I had to change this from `./best_model.keras` to `./best_model.tf`
            keras.callbacks.ModelCheckpoint(filepath='./best_model.tf', monitor='val_accuracy', save_best_only=True)
        ]
    )

# Example usage
if __name__ == "__main__":
    # Check available devices
    print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

    with tf.device('/GPU:0'):
        print("Loading data...")
        data = (["I love this!", "I hate this!"], [0, 1])
        rawTexts = data[0]
        rawLabels = data[1]

        # Preprocess data
        processedData = preprocess_data(rawTexts, rawLabels)
        inputs = processedData['inputs']
        labels = processedData['labels']
        max_len = processedData['max_len']

        print("Data loaded. Max length: ", max_len)

        # Save word_index to a file
        with open('./word_index.json', 'w') as file:
            json.dump(word_index, file)

        model = create_model(max_len)

        print('Training model...')
        train_model(model, inputs, labels, epochs=1, batch_size=32)
        print('Model trained.')

        # When downgrading from TensorFlow 2.18.0 to 2.12.0 I had to change this from `./best_model.keras` to `./best_model.tf`
        model.load_weights('./best_model.tf')
        print('Best model weights loaded.')

        # Save model
        # I think that .h5 extension allows for converting to CoreML, whereas .keras file extension does not
        model.save('./toxic_comment_analysis_model.h5')
        print('Model saved.')

        my_saved_model = tf.keras.models.load_model('./toxic_comment_analysis_model.h5')
        print('Model loaded.')

        print("Making prediction...")
        test_string = "Thank you. I really appreciate it."
        tokenized_string = tokenize(test_string)
        padded_texts = pad_sequences([tokenized_string], max_len)
        tensor = tf.convert_to_tensor(np.array(padded_texts, dtype=np.float32))
        predictions = my_saved_model.predict(tensor)
        print(predictions)
        print("Prediction made.")


        # Convert the Keras model to Core ML
        coreml_model = ct.convert(
            my_saved_model,
            inputs=[ct.TensorType(shape=(max_len,), name="embedding_input", dtype=np.int32)],
            source="tensorflow"
        )

        # Save the Core ML model
        coreml_model.save('toxic_comment_analysis_model.mlmodel')
        print("Model successfully converted to Core ML format.")

Code including Dockerfile & start script as GitHub Gist: https://gist.github.com/fishcharlie/af74d767a3ba1ffbf18cbc6d6a131089

I created a Lemmy community specifically for TensorFlow! Check it out and subscribe if you're interested.

I wanted to provide the community with a quick status update on the development of Echo. This is the longest stretch without an update since Echo was released. This is mostly because I'm currently working on roughly 6+ major new features for Echo that are all in varying stages of completion. (Also because this past week my computer was being repaired, so that took away from being able to work on Echo).

I hope to wrap up at least 1 of these features and get that shipped hopefully this coming week.

Overtime I do anticipate release frequency to slow down. But as part of my goal to build the best Lemmy client for iOS, releases will still occur will regular frequency.

Thank you to everyone who has downloaded the app so far. And to everyone who has given feedback, I really appreciate it. All of your feedback has been heard, and I'm actively working to implement most of it into the application. Stay tuned!

I had to downgrade from TensorFlow 2.18.0 to 2.12.0 recently so that I can turn my model into a CoreML model. And coremltools only supports TensorFlow 2.12.0.

After doing that, training my model is taking roughly 3-4x longer than it did on 2.18.0.

Notifications have arrived to Echo! With version 1.3 available now in the app store you can enable push notifications to receive updates on new posts to communities, or new comments on posts. More push notification options are coming in the future.

What push notification options would you like to see?

After updating to Echo v1.3, in order to enable push notifications for a community or post simply navigate to the community or post you wish you enable notifications for and tap the ellipsis icon, and choose to enable notifications.

Please note that you must be subscribed to Echo+ in order to enable & receive push notifications.

Full release notes:

• Ding Push Notification support is here!
  • Currently supports subscribing to new comments on a post & subscribing to new posts within a community. More to come.
  • Requires Echo+ subscription.
• Now able to view Echo+ benefits even while subscribed.
• General bug fixes, performance improvements, and behind the scenes improvements.
• Updated privacy policy & terms of service.