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Image Sorter App With AI Neural Network And Python

Description

Software of Development using AI, Neural Network, Tensorflow, Keras, Matplotlib and Python.

alt text

Apps

Google Colab

Tools

Matplotlib, Tensorflow, Keras,Numpy, Adam,etc

Usage

$ git clone https://github.com/DanielArturoAlejoAlvarez/AI-Neural-Network-Python3.8-Image-Sorter-App.git[NAME APP]



$ virtualenv env

$ source env/bin/activate

$ pip install -r requirements.txt

$ python3 app.py

Follow the following steps and you're good to go! Important:

alt text

Deep learning is the subset of machine learning methods based on artificial neural networks

Coding

Create Model (AI Neural Network)

...
model = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28,28,1)),
  tf.keras.layers.Dense(50, activation=tf.nn.relu),
  tf.keras.layers.Dense(50, activation=tf.nn.relu),
  tf.keras.layers.Dense(50, activation=tf.nn.softmax)
])

model.compile(
  optimizer='adam',
  loss=tf.keras.losses.SparseCategoricalCrossentropy(),
  metrics=['accuracy']
)
...

Graphical Interface (Matplotlib)

...
def draw_image(i,arr_predictions,label_r,image):
  arr_predictions,label_r,img = arr_predictions[i],label_r[i],image[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img[...,0], cmap=plt.cm.binary)
  label_prediction=np.argmax(arr_predictions)
  if label_prediction == label_r:
    color = 'blue' #Ok prediction
  else:
    color = 'red' #error prediction

  plt.xlabel("{} {:2.0f}% ({})".format(
      class_nanes[label_prediction],
      100*np.max(arr_predictions),
      class_nanes[label_r]),
      color=color
  )

def draw_value_array(i, arr_predictions,label_r):
  arr_predictions,label_r = arr_predictions[i],label_r[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  graphic = plt.bar(range(10),arr_predictions,color="#777777")
  plt.ylim([0,1])
  label_prediction = np.argmax(arr_predictions)
  graphic[label_prediction].set_color("red")
  graphic[label_r].set_color("blue")

files = 5
columns = 5
num_images = files*columns

plt.figure(figsize=(2*2*columns, 2*files))

for i in range(num_images):
  plt.subplot(files, columns*2, i*2+1)
  draw_image(i,predictions,labels_test,images_test)
  plt.subplot(files, columns*2, i*2+2)
  draw_value_array(i,predictions,labels_test)

image = images_test[10]
image = np.array([image])
prediction = model.predict(image)
...

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/DanielArturoAlejoAlvarez/AI-Neural-Network-Python3.8-Image-Sorter-App. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

License

The gem is available as open source under the terms of the MIT License.