# Download the data. The data is already divided into train and test. # The labels are integers representing classes. fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = \  fashion_mnist.load_data() # Names of the integer classes, i.e., 0 -> T-short/top, 1 -> Trouser, etc. class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',   'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] 

 Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 32768/29515 [=================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz 26427392/26421880 [==============================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz 8192/5148 [===============================================] - 0s 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz 4423680/4422102 [==============================] - 0s 0us/step 

print("Shape: ", train_images[0].shape) print("Label: ", train_labels[0], "->", class_names[train_labels[0]]) 

 Shape: (28, 28) Label: 9 -> Ankle boot 

# Reshape the image for the Summary API. img = np.reshape(train_images[0], (-1, 28, 28, 1)) 

# Clear out any prior log data. !rm -rf logs # Sets up a timestamped log directory. logdir = "logs/train_data/" + datetime.now().strftime("%Y%m%d-%H%M%S") # Creates a file writer for the log directory. file_writer = tf.summary.create_file_writer(logdir) # Using the file writer, log the reshaped image. with file_writer.as_default():  tf.summary.image("Training data", img, step=0) 

%tensorboard --logdir logs/train_data 

with file_writer.as_default():  # Don't forget to reshape.  images = np.reshape(train_images[0:25], (-1, 28, 28, 1))  tf.summary.image("25 training data examples", images, max_outputs=25, step=0) %tensorboard --logdir logs/train_data 

# Clear out prior logging data. !rm -rf logs/plots logdir = "logs/plots/" + datetime.now().strftime("%Y%m%d-%H%M%S") file_writer = tf.summary.create_file_writer(logdir) def plot_to_image(figure):  """Converts the matplotlib plot specified by 'figure' to a PNG image and  returns it. The supplied figure is closed and inaccessible after this call."""  # Save the plot to a PNG in memory.  buf = io.BytesIO()  plt.savefig(buf, format='png')  # Closing the figure prevents it from being displayed directly inside  # the notebook.  plt.close(figure)  buf.seek(0)  # Convert PNG buffer to TF image  image = tf.image.decode_png(buf.getvalue(), channels=4)  # Add the batch dimension  image = tf.expand_dims(image, 0)  return image def image_grid():  """Return a 5x5 grid of the MNIST images as a matplotlib figure."""  # Create a figure to contain the plot.  figure = plt.figure(figsize=(10,10))  for i in range(25):  # Start next subplot.  plt.subplot(5, 5, i + 1, title=class_names[train_labels[i]])  plt.xticks([])  plt.yticks([])  plt.grid(False)  plt.imshow(train_images[i], cmap=plt.cm.binary)  return figure # Prepare the plot figure = image_grid() # Convert to image and log with file_writer.as_default():  tf.summary.image("Training data", plot_to_image(figure), step=0) %tensorboard --logdir logs/plots 

model = keras.models.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(32, activation='relu'), keras.layers.Dense(10, activation='softmax') ]) model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) 

def plot_confusion_matrix(cm, class_names):  """  Returns a matplotlib figure containing the plotted confusion matrix.  Args:  cm (array, shape = [n, n]): a confusion matrix of integer classes  class_names (array, shape = [n]): String names of the integer classes  """  figure = plt.figure(figsize=(8, 8))  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)  plt.title("Confusion matrix")  plt.colorbar()  tick_marks = np.arange(len(class_names))  plt.xticks(tick_marks, class_names, rotation=45)  plt.yticks(tick_marks, class_names)  # Compute the labels from the normalized confusion matrix.  labels = np.around(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], decimals=2)  # Use white text if squares are dark; otherwise black.  threshold = cm.max() / 2.  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):  color = "white" if cm[i, j] > threshold else "black"  plt.text(j, i, labels[i, j], horizontalalignment="center", color=color)  plt.tight_layout()  plt.ylabel('True label')  plt.xlabel('Predicted label')  return figure 

# Clear out prior logging data. !rm -rf logs/image logdir = "logs/image/" + datetime.now().strftime("%Y%m%d-%H%M%S") # Define the basic TensorBoard callback. tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir) file_writer_cm = tf.summary.create_file_writer(logdir + '/cm') 

def log_confusion_matrix(epoch, logs): # Use the model to predict the values from the validation dataset. test_pred_raw = model.predict(test_images) test_pred = np.argmax(test_pred_raw, axis=1) # Calculate the confusion matrix. cm = sklearn.metrics.confusion_matrix(test_labels, test_pred) # Log the confusion matrix as an image summary. figure = plot_confusion_matrix(cm, class_names=class_names) cm_image = plot_to_image(figure) # Log the confusion matrix as an image summary. with file_writer_cm.as_default(): tf.summary.image("epoch_confusion_matrix", cm_image, step=epoch) # Define the per-epoch callback. cm_callback = keras.callbacks.LambdaCallback(on_epoch_end=log_confusion_matrix) 

# Start TensorBoard. %tensorboard --logdir logs/image # Train the classifier. model.fit(  train_images,  train_labels,  epochs=5,  verbose=0, # Suppress chatty output  callbacks=[tensorboard_callback, cm_callback],  validation_data=(test_images, test_labels), )