Figure () # or just `fig = tfplot.Figure()` ax = fig. imshow ( heatmap ) return figĪnd do it like: def figure_heatmap ( heatmap ): fig = matplotlib. While the TensorFlow plot operations are usually executed in multi-threaded manners.įor example, avoid any use of pyplot (or plt): # DON'T DO LIKE THIS !!! def figure_heatmap ( heatmap ): fig = plt. This is because pyplot APIs are not thread-safe, Please use object-oriented matplotlib APIs (e.g. Please use this library at your best discernment.
#Tensor flow install for mac code#
Moreover, it might be also a good idea to draw plots from the main code (rather than having a TF op) and add them as image summaries. There is still a room for improvement, which will be addressed in the near future. Matplotlib operations can be very slow as Matplotlib runs in python rather than native code, To grab the latest development version: pip install Some comments on Speed write_add_summary ( summary_pb, global_step = global_step ) Installation pip install tensorflow-plot image_pb ( 'plot_embedding', ) summary_writer. figure_to_array ( fig ) # ndarray uint8 summary_pb = tb. run ( plot_op ) # ndarray uint8 embedding_plot = tfplot. # Get RGB image manually or by executing plot ops. Usage: Manually add summary protos import tensorboard as tb fig, ax =.
#Tensor flow install for mac full#
The full documentation including API docs can be found at readthedocs. Please take a look at the the showcase or examples directory for more examples and use cases. heatmap, figsize = ( 4, 4 ), batch = True ) # function: Tensor -> Tensor plot_op = tf_heatmap ( attention_maps ) # tf.Tensor shape=(?, 400, 400, 4) dtype=uint8 tf. plot ( "heatmap_summary", figure_heatmap, )Įxample of (ii): import tfplot import seaborn.apionly as sns tf_heatmap = tfplot.
plot ( figure_heatmap, , cmap = 'jet' ) # (c) or just directly add an image summary with the plot tfplot. autowrap ( figure_heatmap )( heatmap_tensor ) # tf.Tensor shape=(?, ?, 4) dtype=uint8 # (b) direct invocation similar to tf.py_func plot_op = tfplot. # tf.Tensor shape=(16, 16) dtype=float32 # (a) wrap function as a Tensor factory plot_op = tfplot. colorbar ( im ) return fig heatmap_tensor =. subplots ( figsize = ( 4, 3 )) # DON'T USE plt.subplots() !!!! im = ax. def figure_heatmap ( heatmap, cmap = 'jet' ): # draw a heatmap with a colorbar fig, ax = tfplot. The resulting TensorFlow plot op will be a RGBA image tensor of shape containing the resulting plot. (iii) A method instance of matplotlib Axes Įxample of (i): You can define a python function that takes numpy.ndarray values as input (as an argument of Tensor input),Īnd draw a plot as a return value of.(ii) A python function that has fig or ax keyword parameters (will be auto-injected).(i) A python function that creates and return a matplotlib Figure (see below).We can wrap any pure python function for plotting as a Tensorflow op, such as: float32 ) # tf.Tensor plot_op = plot_scatter ( x, y ) # tf.Tensor shape=(?, ?, 4) dtype=uint8 Usage: Wrap as TF ops scatter ( x, y, color = color ) x = tf. autowrap ( figsize = ( 2, 2 )) def plot_scatter ( x : np. In the body of the wrapped function you can add any logic for drawing plots.
You can directly declare a Tensor factory by using towrap as a decorator. There are two main ways of using tfplot: (i) Use as TF op, and (ii) Manually add summary protos. It allows us to draw any matplotlib plots or figures into images,Īs a part of TensorFlow computation graph.Įspecially, we can easily any plot and see the result image 🚧 Under Construction - API might change! A TensorFlow utility for providing matplotlib-based plot operations
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