System information
- Script can be found below
- MacBook Pro M1 (Mac OS Big Sir (11.5.1))
- TensorFlow installed from (source)
- TensorFlow version (2.5 version) with Metal Support
- Python version: 3.9
- GPU model and memory: MacBook Pro M1 and 16 GB
Steps needed for installing Tensorflow with metal support. https://developer.apple.com/metal/tensorflow-plugin/
I am trying to train a model on Macbook Pro M1, but the performance is so bad and the train doesn't work properly. It takes a ridiculously long time just for a single epoch.
Code needed for reproducing this behavior.
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.layers import Embedding, Dense, LSTM
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Model configuration
additional_metrics = ['accuracy']
batch_size = 128
embedding_output_dims = 15
loss_function = BinaryCrossentropy()
max_sequence_length = 300
num_distinct_words = 5000
number_of_epochs = 5
optimizer = Adam()
validation_split = 0.20
verbosity_mode = 1
# Disable eager execution
tf.compat.v1.disable_eager_execution()
# Load dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_distinct_words)
print(x_train.shape)
print(x_test.shape)
# Pad all sequences
padded_inputs = pad_sequences(x_train, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD>
padded_inputs_test = pad_sequences(x_test, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD>
# Define the Keras model
model = Sequential()
model.add(Embedding(num_distinct_words, embedding_output_dims, input_length=max_sequence_length))
model.add(LSTM(10))
model.add(Dense(1, activation='sigmoid'))
# Compile the model
model.compile(optimizer=optimizer, loss=loss_function, metrics=additional_metrics)
# Give a summary
model.summary()
# Train the model
history = model.fit(padded_inputs, y_train, batch_size=batch_size, epochs=number_of_epochs, verbose=verbosity_mode, validation_split=validation_split)
# Test the model after training
test_results = model.evaluate(padded_inputs_test, y_test, verbose=False)
print(f'Test results - Loss: {test_results[0]} - Accuracy: {100*test_results[1]}%')
I have noticed this same problem with LSTM layers
Also, this issue is been reported in Keras and they can't debug.
Keras issue https://github.com/keras-team/keras/issues/15003
