Tensorflow
Data Modeling using Tensorflow
Advantages
ML Problems
Basic Problems occured while working with Tensorflow
tf.random.set_seed(seed) instead of tf.set_random_seed(seed)
Notations
Let’s annotate this on the plot using the annotate method of the scripting layer or the pyplot interface. We will pass in the following parameters:
s: str, the text of annotation.
xy: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).
xytext: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).
xycoords: The coordinate system that xy is given in – ‘data’ uses the coordinate system of the object being annotated (default).
arrowprops: Takes a dictionary of properties to draw the arrow:
arrowstyle: Specifies the arrow style, '->' is standard arrow.
connectionstyle: Specifies the connection type. arc3 is a straight line.
color: Specify color of arrow.
lw: Specifies the line width.
About Subplot Creation
plot_number is used to identify the particular subplot that this function is to create within the notional grid. plot_number starts at 1, increments across rows first and has a maximum of nrows* ncols as shown below.
Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India’s immigration.
To visualize multiple plots together, we can create a figure (overall canvas) and divide it into subplots, each containing a plot. With subplots, we usually work with the artist layer instead of the scripting layer.