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flowers_model_run.py

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+import tensorflow as tf
+import numpy as np
+
+from flowers_train import class_names
+
+#Loader Parameters
+batch_size = 32
+img_height = 180
+img_width = 180
+
+TF_MODEL_FILE_PATH = 'model.tflite'
+
+def flower_classification(img):
+    interpreter = tf.lite.Interpreter(model_path = TF_MODEL_FILE_PATH)
+
+    #sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
+    #sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
+
+    img_array = tf.keras.utils.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)
+
+    classify_lite = interpreter.get_signature_runner('serving_default')
+    predictions_lite = classify_lite(rescaling_1_input = img_array)['dense_1']
+    score_lite = tf.nn.softmax(predictions_lite)
+    
+    return_msg = "This image most likely belongs to {} with a {:.2f} percent confidence.".format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite))
+    return return_msg
+
+interpreter = tf.lite.Interpreter(model_path = TF_MODEL_FILE_PATH)
+
+sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
+sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
+
+sunflower_img = tf.keras.utils.load_img(
+    sunflower_path, target_size=(img_height, img_width)
+)
+
+img_array = tf.keras.utils.img_to_array(sunflower_img)
+img_array = tf.expand_dims(img_array, 0)
+
+print(interpreter.get_signature_list())
+
+
+classify_lite = interpreter.get_signature_runner('serving_default')
+predictions_lite = classify_lite(rescaling_1_input = img_array)['dense_1']
+score_lite = tf.nn.softmax(predictions_lite)
+
+print(
+    "This image most likely belongs to {} with a {:.2f} percent confidence."
+    .format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite))
+)

flowers_train.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import PIL
+import requests
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+
+import pathlib
+
+
+#Import Data and set directory for the data
+dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
+data_dir = tf.keras.utils.get_file('flower_photos', origin = dataset_url, untar = True)
+data_dir = pathlib.Path(data_dir)
+
+##Print the number of images in the dataset
+image_count = len(list(data_dir.glob('*/*.jpg')))
+print(image_count)
+
+
+##Can access the subset of images containing a certain name/tag
+roses = list(data_dir.glob('roses/*'))
+##Use PIL.Image.open to view the image
+rose_0 = PIL.Image.open(str(roses[0]))
+
+#Loader Parameters
+batch_size = 32
+img_height = 180
+img_width = 180
+
+##Formalize the training dataset
+train_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split = 0.2,
+    subset = "validation",
+    seed = 123,
+    image_size = (img_height, img_width),
+    batch_size = batch_size
+)
+
+##Formalize the validation dataset
+val_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split = 0.2,
+    subset = 'validation',
+    seed = 123,
+    image_size = (img_height, img_width),
+    batch_size = batch_size
+)
+
+## Printing class names
+class_names = train_ds.class_names
+#print(class_names)
+
+##Autotunes the value of data dynamically at runtime
+AUTOTUNE = tf.data.AUTOTUNE
+
+train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size = AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size = AUTOTUNE)
+
+normalization_layer = layers.Rescaling(1./255)
+
+normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+
+##Keras Model
+num_classes = len(class_names)
+
+model = Sequential([
+    layers.Rescaling(1./255, input_shape = (img_height, img_width, 3)),
+    layers.Conv2D(16, 3, padding = 'same', activation = 'relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation = 'relu'),
+    layers.Dense(num_classes)
+])
+
+##Setting framework for the loss functions/optimization of tuning
+model.compile(optimizer = 'adam',
+              loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),
+              metrics = ['accuracy'])
+
+
+##This is the model framework
+print(model.summary())
+
+
+##Training the model for 10 epochs
+epochs = 10
+history = model.fit(
+    train_ds,
+    validation_data = val_ds,
+    epochs = epochs
+)
+
+##Analyze results
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs_range = range(epochs)
+
+##Visualize training stats
+# plt.figure(figsize = (8,8))
+# plt.subplot(1, 2, 1)
+# plt.plot(epochs_range, acc, label = 'Training Accuracy')
+# plt.plot(epochs_range, val_acc, label = 'Validation Accuracy')
+# plt.legend(loc = 'lower right')
+# plt.title('Training and Validation Accuracy')
+
+# plt.subplot(1, 2, 2)
+# plt.plot(epochs_range, loss, label= 'Training Loss')
+# plt.plot(epochs_range, val_loss, label= 'Validation Loss')
+# plt.legend(loc = 'upper right')
+# plt.title('Training and Validation Loss')
+# plt.show()
+
+##Predict on new data
+sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
+sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
+
+sunflower_img = tf.keras.utils.load_img(
+    sunflower_path, target_size=(img_height, img_width)
+)
+
+img_array = tf.keras.utils.img_to_array(sunflower_img)
+img_array = tf.expand_dims(img_array, 0)
+
+predictions = model.predict(img_array)
+score = tf.nn.softmax(predictions[0])
+
+print(
+    "This image most likely belongs to {} with a {:.2f} percent confidence."
+    .format(class_names[np.argmax(score)], 100 * np.max(score))
+)
+
+
+##Convert model to TensorflowLite Model
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+tflite_model = converter.convert()
+
+##Save model to be used again
+with open('model.tflite', 'wb') as f:
+    f.write(tflite_model)
+

model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:245150e42934566332f6e599d74400f82a73417f1311f43a7ac50346142fc4f1
+size 15961108

requirements.txt

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+matplotlib == 3.6.2
+numpy
+pillow
+tensorflow
+pathlib
+gradio