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Diffstat (limited to '.virtual_documents/notebooks')
3 files changed, 204 insertions, 0 deletions
diff --git a/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb b/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb new file mode 100644 index 0000000..adeabf5 --- /dev/null +++ b/.virtual_documents/notebooks/IBM Watson Visual Recognition.ipynb @@ -0,0 +1,75 @@ + + + +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 new file mode 100644 index 0000000..5f6d4b3 --- /dev/null +++ b/.virtual_documents/notebooks/TensorFlow_QuickStart.ipynb @@ -0,0 +1,82 @@ + + + + + + +# 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 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() + + + + + + + + + + + + |