add .gitignore

1 files changed, 0 insertions, 47 deletions

diff --git a/.virtual_documents/notebooks/Untitled.ipynb b/.virtual_documents/notebooks/Untitled.ipynb
deleted file mode 100644
index 7d6f130..0000000
--- a/.virtual_documents/notebooks/Untitled.ipynb
+++ /dev/null
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-# pip3 install tensorflow
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-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)
-])
-
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-# 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
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-
-# The tf.nn.softmax function converts these logits to probabilities for each class: 
-tf.nn.softmax(predictions).numpy()
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