Echo v1.6 has been released with support for searching Reddit communities.

Simply navigate to the Explore tab and enter a subreddit name (e.g., r/technology) or a link to the subreddit to find Lemmy community alternatives.

This is only the beginning. Stay tuned for more!

Below are the full release notes.

```

• Introducing: Search Reddit Communities
  • Have a favorite community on Reddit and want to find similar Lemmy communities? Simply search for the community in the Explore tab, and see similar Lemmy communities.
  • For example, search for r/apple or r/worldnews.
• General bug fixes, performance improvements, and behind the scenes improvements. ```

!Screenshot of a search in Echo showing the query “r/technology” in the search bar. Under the “Communities” section, a single result appears: a community named “Technology” with a yellow hexagon icon featuring a black microchip symbol.

I'm trying to train a machine learning model to detect if an image is blurred or not.

I have 11,798 unblurred images, and I have a script to blur them and then use that to train my model.

However when I run the exact same training 5 times the results are wildly inconsistent (as you can see below). It also only gets to 98.67% accuracy max.

I'm pretty new to machine learning, so maybe I'm doing something really wrong. But coming from a software engineering background and just starting to learn machine learning, I have tons of questions. It's a struggle to know why it's so inconsistent between runs. It's a struggle to know how good is good enough (ie. when should I deploy the model). It's a struggle to know how to continue to improve the accuracy and make the model better.

Any advice or insight would be greatly appreciated.

View all the code: https://gist.github.com/fishcharlie/68e808c45537d79b4f4d33c26e2391dd

!

Echo v1.5 has been released with support for uploading images as part of your Echo post.

This release also includes support for Apple's new Apple Intelligence Image Playground feature. Generate new images based on an existing image or create a brand new image of anything you want. Echo has also created a new Lemmy community for Image Playground Showcase: !ip_showcase@eventfrontier.com. Be sure to join and post all your best (or worst) Image Playground images.

We aren't done yet! Much more coming soon. We are committed to building the best, native first, Lemmy experience for iOS.

Below are the full release notes.

```

• Support for uploading image in post.
  • Requires your Lemmy instance to support pictrs at the default URL.
• Adds support for Image Playground. Generate image from an existing image or create a new image using Apple Intelligence.
  • Requires iOS 18.2, an Apple Intelligence supported device, and access to be granted to Image Playground.
• Fixes an issue where reply & bookmark options wouldn't show up in comment ellipsis menu.
• Fixes an issue where sometimes when closing a screen multiple screens would close unintentionally.
• Fixes an issue where sometimes avatar images would not display correctly.
• General bug fixes, performance improvements, and behind the scenes improvements. ```

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? > > --- > > python > 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?

---

```python 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.

Dodgers beat the Yankees 4-2 as the series shifts to Yankee Stadium.

Are the Yankees in desperation mode yet? Judge doesn’t look good at the plate.