From 5e2bb53d528d60e0a44607377fa3d09553630d5b Mon Sep 17 00:00:00 2001 From: Christian Cleberg Date: Mon, 18 Sep 2023 20:53:06 -0500 Subject: add tensorflow notebook --- .virtual_documents/notebooks/Untitled.ipynb | 47 +++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 .virtual_documents/notebooks/Untitled.ipynb (limited to '.virtual_documents/notebooks/Untitled.ipynb') diff --git a/.virtual_documents/notebooks/Untitled.ipynb b/.virtual_documents/notebooks/Untitled.ipynb new file mode 100644 index 0000000..7d6f130 --- /dev/null +++ b/.virtual_documents/notebooks/Untitled.ipynb @@ -0,0 +1,47 @@ + + + +# pip3 install tensorflow + + +import tensorflow as tf +print("TensorFlow version:", tf.__version__) + + +# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255. +# Scale these values to a range of 0 to 1 by dividing the values by 255.0. +# This also converts the sample data from integers to floating-point numbers: +mnist = tf.keras.datasets.mnist + +(x_train, y_train), (x_test, y_test) = mnist.load_data() +x_train, x_test = x_train / 255.0, x_test / 255.0 + + +# Build a tf.keras.Sequential model: +model = tf.keras.models.Sequential([ + tf.keras.layers.Flatten(input_shape=(28, 28)), + tf.keras.layers.Dense(128, activation='relu'), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(10) +]) + + +# For each example, the model returns a vector of logits or log-odds scores, one for each class. +predictions = model(x_train[:1]).numpy() +predictions + + +# The tf.nn.softmax function converts these logits to probabilities for each class: +tf.nn.softmax(predictions).numpy() + + + + + + + + + + + + -- cgit v1.2.3-70-g09d2