DataBloom

I was wondering ,when creating an image classifer with tflite-model-maker, what is the optimal size for the images in the datasets . Thanks 🙂

Hi all. I am trying to train a neural net to perform handwritten character recognition, and have attached the relevant code below. I’m training 28×28 size characters with the EMNIST dataset. No matter how what I change the batch size to, when I try to train the model, I always get a similar error at the bottom. Does anyone know how to fix this? Thank you for the help!

def load_dataset(): X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)) X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)) y_train = to_categorical(y_train) y_test = to_categorical(y_test) return X_train, y_train, X_test, y_test def prep_pixels(train, test): train_norm = train.astype('float32') test_norm = test.astype('float32') train_norm = train_norm/255.0 test_norm = test_norm/255.0 return train_norm, test_norm def define_model(): model = Sequential() model.add(Conv2D(32, (3,3), activation = 'relu', kernel_initializer = 'he_uniform', input_shape = (28, 28, 1))) model.add(MaxPooling2D((2,2))) model.add(Flatten()) model.add(Dense(100, activation = 'relu', kernel_initializer = 'he_uniform')) model.add(Dense(46, activation = 'softmax')) opt = SGD(learning_rate = 0.01, momentum = 0.9) model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy']) return model def evaluate_model(dataX, dataY, xtest, ytest, n_folds = 5): model = define_model() history = model.fit(dataX, dataY, epochs = 4, batch_size = 64, validation_data = (xtest, ytest)) _, acc = model.evaluate(dataX, dataY, verbose=0) scores.append(acc) histories.append(history) 

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 ValueError: Shapes (64, 1) and (64, 46) are incompatible 

EDIT: Fixed some bugs, but still have the same error

I have been following the tensorflow examples on how to set up a model for pruning and quantise it in order to improve inference. What I noticed however was:

1) the sparse model resulted from pruning has no faster inference benefits

2) the quantisation makes the model even slower (I know that this is probably due to TFlite not being optimised for x86).

What is the method you use to prune your models?

I have two classes and trying to build Tensorflow ranking model

when i run model.fit(cached_train, epochs=3), i get error

ValueError: The first argument to `Layer.call` must always be passed

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class ProdRankingModel(tf.keras.Model):

def __init__(self):

super().__init__()

embedding_dimension = 32

self.user_embeddings = tf.keras.Sequential([

tf.keras.layers.StringLookup(

vocabulary=unique_user_ids, mask_token=None),

tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)])

self.prod_embeddings = tf.keras.Sequential([

tf.keras.layers.StringLookup(

vocabulary=unique_items, mask_token=None),

tf.keras.layers.Embedding(len(unique_items) + 1, embedding_dimension)

])

# Compute predictions.

self.ratings = tf.keras.Sequential([

tf.keras.layers.Dense(256, activation=”relu”),

tf.keras.layers.Dense(64, activation=”relu”),

tf.keras.layers.Dense(1)

])

def call(self, inputs):

user_id, products = inputs

​

user_embedding = self.user_embeddings(user_id)

product_embedding = self.prod_embeddings(products)

return self.ratings(tf.concat([user_embedding, product_embedding], axis=1))

​

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class ProductModel(tfrs.models.Model):

def __init__(self):

super().__init__()

self.prodranking_model: tf.keras.Model = ProdRankingModel()

self.task: tf.keras.layers.Layer = tfrs.tasks.Ranking(

loss = tf.keras.losses.MeanSquaredError(),

metrics=[tf.keras.metrics.RootMeanSquaredError()]

)

​

def call(self, features: Dict[str, tf.Tensor]) -> tf.Tensor:

return self.prodranking_model(

(features[“user_id”], features[“prod_name”]))

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def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:

labels = features.pop(“prod_count”)

rating_predictions = self(features)

# The task computes the loss and the metrics.

return self.task(labels=labels, predictions=rating_predictions)

Hi! I’m building a CNN for the first time for a university project: the idea is to classify images coming from 10 animal classes (taken from ImageNet).

Anybody willing to give me some advice about my model? Here’s my model:

model = Sequential() model.add(layers.InputLayer(input_shape=(224,224,3))) model.add(layers.BatchNormalization()) model.add(layers.Conv2D(64,(3,3), activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv2D(64,(3,3),activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv2D(128,(3,3),activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Conv2D(256,(3,3),activation='relu')) model.add(layers.AvgPool2D(2,2)) model.add(layers.Flatten(name='features_layer')) model.add(layers.Dense(10, activation='softmax')) 

Training Loss keeps decreasing and training accuracy keeps improving (and even converges to 1) but after ~10 epochs I’m stuck around 0.5 validation accuracy. The actual dataset contains 2500 images, and I tried both with a 0.2 validation split and 0.3. Using 0.2 leads to more stable results, but reach 0.5 val_accuracy more slowly. I can try augmenting my dataset (creating some slightly modified copies of the images I have), but the training time increases quite a lot, and I would like to be reasonably sure that my model is good (at least on theory) before training with high number of epochs and new expanded dataset.

Is the overall structure of my net correct? Some questions:

• Should the number of convolutional layers and the number of kernel used in each be set ‘by heart’? I’ve seen CNNs with very different architectures when comes to number of Conv2D layers included, and I’m not getting if there’s some heuristics I should follow.
• Should I use one batch normalization layer at the end of the convolutions, or should I go with a batch normalization after each Conv2D?

Sorry for the bunch of ‘noob’ questions, I hope you’ll understand my perplexity. As I said, it is my first time dealing with building a CNN myself and I feel that even real ‘general’ suggestions might help. I would really appreciate any advice c:

I faced an error when I was building Tensorflow from the source with my laptop. It has only 8GB RAM and out of memory is the reason for the error. I’m going to buy a new laptop but I’m not sure how much memory Tensorflow requires. MacBook Air is one of the candidates. I would be happy if I know something about it. Thanks.

I have converted 2 csv files into arrays in the hopes that I can create a positive training sample. Could someone help me find a way to combine anchors and matches, so that they are a positive training sample. This would be hugely appreciated as I am quite stuck.

## Convert csv to tensor

anchor = train
anchors = np.array(anchor)
match = train2
matches = np.array(match)

# Create positive training sample

??????

I read about Grappler and that it can optimize TF models, which is an interesting feature.

I use keras to create, train and save models, and also convert them to tflite in order to deploy them on other systems. My question is, whether this optimization is already integrated into keras and / or the tflite conversion.

It seems like quite a big topic to get into, so if it already happens automatically, I don’t want to spend so much time on it.