| import tensorflow as tf | |
| from tensorflow import keras | |
| # Two inputs - One Output | |
| # Image | |
| input_1 = tf.keras.layers.Input(shape=(28, 28, 1)) | |
| conv2d_1 = tf.keras.layers.Conv2D(64, kernel_size=3, | |
| activation=tf.keras.activations.relu)(input_1) | |
| # Second conv layer : |
| from sklearn.model_selection import train_test_split | |
| import numpy as np | |
| import tensorflow as tf | |
| def create_dataset(X, Y, batch_size): | |
| """ Create train and test TF dataset from X and Y | |
| The prefetch overlays the preprocessing and model execution of a training step. | |
| While the model is executing training step s, the input pipeline is reading the data for step s+1. | |
| AUTOTUNE automatically tune the number for sample which are prefeteched automatically. |
| import matplotlib.pyplot as plt | |
| # this is the setting for plots for research paper and articles. | |
| %matplotlib inline | |
| %config InlineBackend.figure_format = 'retina' | |
| # Set the global font to be DejaVu Sans, size 10 (or any other sans-serif font of your choice!) | |
| plt.rc('font',**{'family':'sans-serif','sans-serif':['DejaVu Sans'],'size':10}) | |
| # Set the font used for MathJax - more on this later |
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