TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started. Python programs are run directly in the browser—a great way to learn and use TensorFlow.
The mathematical concept of a tensor could be broadly explained in the following way: If a scalar has the lowest dimensionality and is followed by a vector and then by a matrix. A tensor would be the next object in the line. Scalar, vectors and matrices are all tensors of rank 0, 1 and 2 respectively. Tensors are simply a generalization of the concepts we have seen so far.
At first, computation in TensorFlow may seem needlessly complicated. But there is a reason for it: because of how TensorFlow treats computation, developing more complicated algorithms is relatively easy. We will look into the pseudocode of a TensorFlow algorithm.
we will introduce the general flow of TensorFlow algorithms.
avar = tf.constant(42)_
xinput = tf.placeholder(tf.float32, [None, input_size])_
yinput = tf.placeholder(tf.float32, [None, num_classes])_
6. *Define the model structure: *After we have the data, and have initialized our variables and placeholders, we have to define the model. This is done by building a computational graph. TensorFlow chooses what operations and values must be the variables and placeholders to arrive at our model outcomes.
ypred = tf.add(tf.mul(x_input, weight_matrix), b_matrix)_
7. Declare the loss functions: After defining the model, we must be able to evaluate the output. This is where we declare the loss function. The loss function is very important as it tells us how far off our predictions are from the actual values.
loss = tf.reduce_mean(tf.square(y_actual — y_pred))
8.*Initialize and train the model: *Now that we have everything in place, we need to create an instance of our graph, feed in the data through the placeholders, and let TensorFlow change the variables to better predict our training data.
with tf.Session(graph=graph) as session:
Note that we can also initiate our graph with:
session = tf.Session(graph=graph)
The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.
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