Introduction to Computer Vision with TensorFlow Reviews

Sentot S. · Reviewed 4 months ago

Ricky M. · Reviewed 4 months ago

Sentot S. · Reviewed 4 months ago

CARAKA R. · Reviewed 4 months ago

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Oliver G. · Reviewed 4 months ago

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Rida A. · Reviewed 4 months ago

I've found a solution, on Task 1 instead of using "pip install --upgrade tensorflow", I used the previous version of tensorflow "pip install tensorflow==2.15.0" and then it worked

Asep h. · Reviewed 4 months ago

the code isn't updated

Chanat A. · Reviewed 4 months ago

Omer M. · Reviewed 4 months ago

Ahmad B. · Reviewed 4 months ago

Maura A. · Reviewed 4 months ago

Felix S. · Reviewed 4 months ago

update course to support new tensorflow version, my code successfully : # Import and configure logging import logging import google.cloud.logging as cloud_logging from google.cloud.logging.handlers import CloudLoggingHandler from google.cloud.logging_v2.handlers import setup_logging # Setup Google Cloud Logging cloud_logger = logging.getLogger('cloudLogger') cloud_logger.setLevel(logging.INFO) client = cloud_logging.Client() cloud_logger.addHandler(CloudLoggingHandler(client)) cloud_logger.addHandler(logging.StreamHandler()) # Import TensorFlow and other required libraries import tensorflow as tf import tensorflow_datasets as tfds import numpy as np # Define, load and configure data (ds_train, ds_test), info = tfds.load('fashion_mnist', split=['train', 'test'], with_info=True, as_supervised=True) # Values before normalization image_batch, labels_batch = next(iter(ds_train)) print("Before normalization ->", np.min(image_batch[0]), np.max(image_batch[0])) # Define batch size BATCH_SIZE = 32 # Normalize and batch process the dataset ds_train = ds_train.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y)).batch(BATCH_SIZE) ds_test = ds_test.map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y)).batch(BATCH_SIZE) # Examine the min and max values of the batch after normalization image_batch, labels_batch = next(iter(ds_train)) print("After normalization ->", np.min(image_batch[0]), np.max(image_batch[0])) # Define the model model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28, 1)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]) # Train the model model.fit(ds_train, epochs=5) # Evaluate the model evaluation = model.evaluate(ds_test) cloud_logger.info(f"Model evaluation: {evaluation}") # Save the entire model to a Keras V3 file model.save('model.keras') # Save the entire model to a HDF5 file model.save('model.h5') # Recreate the exact same model from the Keras V3 file new_model_keras = tf.keras.models.load_model('model.keras') # Summary of loaded Keras V3 model new_model_keras.summary() # Recreate the exact same model from the HDF5 file new_model_h5 = tf.keras.models.load_model('model.h5') # Summary of loaded HDF5 model new_model_h5.summary()

Asep h. · Reviewed 4 months ago

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Swapnil-Kapile D. · Reviewed 4 months ago

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Nikhil G. · Reviewed 4 months ago

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Swapnil-Kapile D. · Reviewed 4 months ago