High-level API's are extremely important in all software development because they provide simple abstractions for doing very complicated tasks. This makes it easier to write and understand your source-code, and it lowers the risk of errors.
In Tutorial #03 we saw how to use various builder API's for creating Neural Networks in TensorFlow. However, there was a lot of additional code required for training the models and using them on new data. The Estimator is another high-level API that implements most of this, although it can be debated how simple it really is.
Using the Estimator API consists of several steps:
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
This was developed using Python 3.6 (Anaconda) and TensorFlow version:
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tf.__version__
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The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path.
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from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/MNIST/', one_hot=True)
The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial.
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print("Size of:")
print("- Training-set:\t\t{}".format(len(data.train.labels)))
print("- Test-set:\t\t{}".format(len(data.test.labels)))
print("- Validation-set:\t{}".format(len(data.validation.labels)))
The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers so we calculate that now.
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data.train.cls = np.argmax(data.train.labels, axis=1)
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data.test.cls = np.argmax(data.test.labels, axis=1)
This is an example of one-hot encoded labels:
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data.train.labels[0:10]
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These are the corresponding class-numbers:
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data.train.cls[0:10]
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The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below.
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# We know that MNIST images are 28 pixels in each dimension.
img_size = 28
# Images are stored in one-dimensional arrays of this length.
img_size_flat = img_size * img_size
# Tuple with height and width of images used to reshape arrays.
img_shape = (img_size, img_size)
# Number of colour channels for the images: 1 channel for gray-scale.
num_channels = 1
# Number of classes, one class for each of 10 digits.
num_classes = 10
Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image.
In [10]:
def plot_images(images, cls_true, cls_pred=None):
assert len(images) == len(cls_true) == 9
# Create figure with 3x3 sub-plots.
fig, axes = plt.subplots(3, 3)
fig.subplots_adjust(hspace=0.3, wspace=0.3)
for i, ax in enumerate(axes.flat):
# Plot image.
ax.imshow(images[i].reshape(img_shape), cmap='binary')
# Show true and predicted classes.
if cls_pred is None:
xlabel = "True: {0}".format(cls_true[i])
else:
xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])
# Show the classes as the label on the x-axis.
ax.set_xlabel(xlabel)
# Remove ticks from the plot.
ax.set_xticks([])
ax.set_yticks([])
# Ensure the plot is shown correctly with multiple plots
# in a single Notebook cell.
plt.show()
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# Get the first images from the test-set.
images = data.test.images[0:9]
# Get the true classes for those images.
cls_true = data.test.cls[0:9]
# Plot the images and labels using our helper-function above.
plot_images(images=images, cls_true=cls_true)
Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.
Note that we will create an Estimator using the DNNClassifier which assumes the class-numbers are integers so we use data.train.cls instead of data.train.labels which are one-hot encoded arrays.
The function also has parameters for batch_size, queue_capacity and num_threads for finer control of the data reading. In our case we take the data directly from a numpy array in memory, so it is not needed.
In [12]:
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(data.train.images)},
y=np.array(data.train.cls),
num_epochs=None,
shuffle=True)
This actually returns a function:
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train_input_fn
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Calling this function returns a tuple with TensorFlow ops for returning the input and output data:
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train_input_fn()
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Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so num_epochs=1 and we do not want the images shuffled so shuffle=False.
In [15]:
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(data.test.images)},
y=np.array(data.test.cls),
num_epochs=1,
shuffle=False)
An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.
In [16]:
some_images = data.test.images[0:9]
In [17]:
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": some_images},
num_epochs=1,
shuffle=False)
The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below.
In [18]:
some_images_cls = data.test.cls[0:9]
In [19]:
feature_x = tf.feature_column.numeric_column("x", shape=img_shape)
You can have several input features which would then be combined in a list:
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feature_columns = [feature_x]
In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively.
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num_hidden_units = [512, 256, 128]
The DNNClassifier then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved.
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model = tf.estimator.DNNClassifier(feature_columns=feature_columns,
hidden_units=num_hidden_units,
activation_fn=tf.nn.relu,
n_classes=num_classes,
model_dir="./checkpoints_tutorial17-1/")
We can now train the model for a given number of iterations. This automatically loads and saves checkpoints so we can continue the training later.
Note that the text INFO:tensorflow: is printed on every line and makes it harder to quickly read the actual progress. It should have been printed on a single line instead.
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model.train(input_fn=train_input_fn, steps=2000)
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In [24]:
result = model.evaluate(input_fn=test_input_fn)
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result
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In [26]:
print("Classification accuracy: {0:.2%}".format(result["accuracy"]))
The trained model can also be used to make predictions on new data.
Note that the TensorFlow graph is recreated and the checkpoint is reloaded every time we make predictions on new data. If the model is very large then this could add a significant overhead.
It is unclear why the Estimator is designed this way, possibly because it will always use the latest checkpoint and it can also be distributed easily for use on multiple computers.
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predictions = model.predict(input_fn=predict_input_fn)
In [28]:
cls = [p['classes'] for p in predictions]
In [29]:
cls_pred = np.array(cls, dtype='int').squeeze()
cls_pred
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In [30]:
plot_images(images=some_images,
cls_true=some_images_cls,
cls_pred=cls_pred)
If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:
The Estimator can be run in three modes: Training, Evaluation, or Prediction. The code is mostly the same, but in Prediction-mode we do not need to setup the loss-function and optimizer.
This is another aspect of the Estimator API that is poorly designed and resembles how we did ANSI C programming using structs in the old days. It would probably have been more elegant to split this into several functions and sub-classed the Estimator-class.
In [31]:
def model_fn(features, labels, mode, params):
# Args:
#
# features: This is the x-arg from the input_fn.
# labels: This is the y-arg from the input_fn,
# see e.g. train_input_fn for these two.
# mode: Either TRAIN, EVAL, or PREDICT
# params: User-defined hyper-parameters, e.g. learning-rate.
# Reference to the tensor named "x" in the input-function.
x = features["x"]
# The convolutional layers expect 4-rank tensors
# but x is a 2-rank tensor, so reshape it.
net = tf.reshape(x, [-1, img_size, img_size, num_channels])
# First convolutional layer.
net = tf.layers.conv2d(inputs=net, name='layer_conv1',
filters=16, kernel_size=5,
padding='same', activation=tf.nn.relu)
net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)
# Second convolutional layer.
net = tf.layers.conv2d(inputs=net, name='layer_conv2',
filters=36, kernel_size=5,
padding='same', activation=tf.nn.relu)
net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)
# Flatten to a 2-rank tensor.
net = tf.contrib.layers.flatten(net)
# Eventually this should be replaced with:
# net = tf.layers.flatten(net)
# First fully-connected / dense layer.
# This uses the ReLU activation function.
net = tf.layers.dense(inputs=net, name='layer_fc1',
units=128, activation=tf.nn.relu)
# Second fully-connected / dense layer.
# This is the last layer so it does not use an activation function.
net = tf.layers.dense(inputs=net, name='layer_fc2',
units=10)
# Logits output of the neural network.
logits = net
# Softmax output of the neural network.
y_pred = tf.nn.softmax(logits=logits)
# Classification output of the neural network.
y_pred_cls = tf.argmax(y_pred, axis=1)
if mode == tf.estimator.ModeKeys.PREDICT:
# If the estimator is supposed to be in prediction-mode
# then use the predicted class-number that is output by
# the neural network. Optimization etc. is not needed.
spec = tf.estimator.EstimatorSpec(mode=mode,
predictions=y_pred_cls)
else:
# Otherwise the estimator is supposed to be in either
# training or evaluation-mode. Note that the loss-function
# is also required in Evaluation mode.
# Define the loss-function to be optimized, by first
# calculating the cross-entropy between the output of
# the neural network and the true labels for the input data.
# This gives the cross-entropy for each image in the batch.
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,
logits=logits)
# Reduce the cross-entropy batch-tensor to a single number
# which can be used in optimization of the neural network.
loss = tf.reduce_mean(cross_entropy)
# Define the optimizer for improving the neural network.
optimizer = tf.train.AdamOptimizer(learning_rate=params["learning_rate"])
# Get the TensorFlow op for doing a single optimization step.
train_op = optimizer.minimize(
loss=loss, global_step=tf.train.get_global_step())
# Define the evaluation metrics,
# in this case the classification accuracy.
metrics = \
{
"accuracy": tf.metrics.accuracy(labels, y_pred_cls)
}
# Wrap all of this in an EstimatorSpec.
spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics)
return spec
In [32]:
params = {"learning_rate": 1e-4}
We can then create an instance of the new Estimator.
Note that we don't provide feature-columns here as it is inferred automatically from the data-functions when model_fn() is called.
It is unclear from the TensorFlow documentation why it is necessary to specify the feature-columns when using DNNClassifier in the example above, when it is not needed here.
In [33]:
model = tf.estimator.Estimator(model_fn=model_fn,
params=params,
model_dir="./checkpoints_tutorial17-2/")
In [34]:
model.train(input_fn=train_input_fn, steps=2000)
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In [35]:
result = model.evaluate(input_fn=test_input_fn)
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result
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In [37]:
print("Classification accuracy: {0:.2%}".format(result["accuracy"]))
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predictions = model.predict(input_fn=predict_input_fn)
In [39]:
cls_pred = np.array(list(predictions))
cls_pred
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In [40]:
plot_images(images=some_images,
cls_true=some_images_cls,
cls_pred=cls_pred)
This tutorial showed how to use the Estimator API in TensorFlow. It is supposed to make it easier to train and use a model, but it seems to have several design problems:
It seems that the Estimator API could have been much simpler and easier to use. For small projects you may find it too complicated and confusing to be worth the effort. But it is possible that the Estimator API is useful if you have a very large dataset and if you train on many machines.
These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.
You may want to backup this Notebook before making any changes.
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