add .gitignore

3 files changed, 0 insertions, 204 deletions

diff --git a/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb b/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb
deleted file mode 100644
index adeabf5..0000000
--- a/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb
+++ /dev/null
@@ -1,75 +0,0 @@
-
-
-
-pip install --upgrade --user "ibm-watson>=4.5.0"
-
-
-apikey = "<your-apikey>"
-version = "2018-03-19"
-url = "<your-url>"
-
-
-import json
-from ibm_watson import VisualRecognitionV3
-from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
-
-authenticator = IAMAuthenticator(apikey)
-visual_recognition = VisualRecognitionV3(
-    version=version,
-    authenticator=authenticator
-)
-
-visual_recognition.set_service_url(url)
-
-
-visual_recognition.set_default_headers({'x-watson-learning-opt-out': "true"})
-
-
-data = [
-{
-  "title": "Bear Country, South Dakota",
-  "url": "https://example.com/photos/highres/20140717.jpg"
-},
-{
-  "title": "Pactola Lake",
-  "url": "https://example.com/photos/highres/20140718.jpg"
-},
-{
-  "title": "Welcome to Utah",
-  "url": "https://example.com/photos/highres/20190608_02.jpg"
-},
-{
-  "title": "Honey Badger",
-  "url": "https://example.com/photos/highres/20190611_03.jpg"
-},
-{
-  "title": "Grand Canyon Lizard",
-  "url": "https://example.com/photos/highres/20190612.jpg"
-},
-{
-  "title": "The Workhouse",
-  "url": "https://example.com/photos/highres/20191116_01.jpg"
-}
-]
-
-
-from ibm_watson import ApiException
-
-for x in range(len(data)):
-    try:
-        url = data[x]["url"]
-        images_filename = data[x]["title"]
-        classes = visual_recognition.classify(
-            url=url,
-            images_filename=images_filename,
-            threshold='0.6',
-            owners=["IBM"]).get_result()
-        print("-------------------------------------------------------------------------------------------------------------------------------------")
-        print("Image Title: ", data[x]["title"], "\n")
-        print("Image URL: ", data[x]["url"], "\n")
-        classification_results = classes["images"][0]["classifiers"][0]["classes"]
-        for result in classification_results:
-            print(result["class"], "(", result["score"], ")")
-        print("-------------------------------------------------------------------------------------------------------------------------------------")
-    except ApiException as ex:
-        print("Method failed with status code " + str(ex.code) + ": " + ex.message)
diff --git a/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb b/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb
deleted file mode 100644
index 5f6d4b3..0000000
--- a/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-
-
-
-
-
-
-# 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
-
-
-# You can preview the raw data prior to training the model
-print(mnist.load_data())
-
-
-
-
-
-# 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()
-
-
-# Define a loss function for training using losses.SparseCategoricalCrossentropy:
-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
-
-# Configure and compile the model
-model.compile(optimizer='adam',
-              loss=loss_fn,
-              metrics=['accuracy'])
-
-
-
-
-
-
-# Use the Model.fit method to adjust your model parameters and minimize the loss: 
-model.fit(x_train, y_train, epochs=5)
-
-
-# The Model.evaluate method checks the model's performance, usually on a validation set or test set.
-model.evaluate(x_test,  y_test, verbose=2)
-
-
-# If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:
-
-probability_model = tf.keras.Sequential([
-  model,
-  tf.keras.layers.Softmax()
-])
-probability_model(x_test[:5])
-
-
-
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
@@ -1,47 +0,0 @@
-
-
-
-# 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()
-
-
-
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-