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"""Untitled0.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1kQ-r8F4JDUetydbdTfy050X-itaMuCNN |
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""" |
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!pip install gradio |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import os |
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import PIL |
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import tensorflow as tf |
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from tensorflow import keras |
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from tensorflow.keras import layers |
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from tensorflow.keras.models import Sequential |
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import pathlib |
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dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz" |
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data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True) |
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data_dir = pathlib.Path(data_dir) |
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roses = list(data_dir.glob('roses/*')) |
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print(roses[0]) |
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PIL.Image.open(str(roses[0])) |
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img_height,img_width=180,180 |
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batch_size=32 |
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train_ds = tf.keras.preprocessing.image_dataset_from_directory( |
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data_dir, |
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validation_split=0.2, |
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subset="training", |
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seed=123, |
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image_size=(img_height, img_width), |
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batch_size=batch_size) |
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val_ds = tf.keras.preprocessing.image_dataset_from_directory( |
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data_dir, |
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validation_split=0.2, |
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subset="validation", |
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seed=123, |
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image_size=(img_height, img_width), |
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batch_size=batch_size) |
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class_names = train_ds.class_names |
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print(class_names) |
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import matplotlib.pyplot as plt |
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plt.figure(figsize=(10, 10)) |
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for images, labels in train_ds.take(1): |
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for i in range(9): |
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ax = plt.subplot(3, 3, i + 1) |
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plt.imshow(images[i].numpy().astype("uint8")) |
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plt.title(class_names[labels[i]]) |
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plt.axis("off") |
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num_classes = 5 |
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model = Sequential([ |
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layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)), |
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layers.Conv2D(16, 3, padding='same', activation='relu'), |
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layers.MaxPooling2D(), |
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layers.Conv2D(32, 3, padding='same', activation='relu'), |
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layers.MaxPooling2D(), |
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layers.Conv2D(64, 3, padding='same', activation='relu'), |
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layers.MaxPooling2D(), |
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layers.Flatten(), |
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layers.Dense(128, activation='relu'), |
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layers.Dense(num_classes,activation='softmax') |
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]) |
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model.compile(optimizer='adam', |
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
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metrics=['accuracy']) |
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epochs=10 |
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history = model.fit( |
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train_ds, |
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validation_data=val_ds, |
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epochs=epochs |
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) |
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def predict_image(img): |
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img_4d=img.reshape(-1,180,180,3) |
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prediction=model.predict(img_4d)[0] |
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return {class_names[i]: float(prediction[i]) for i in range(5)} |
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image = gr.inputs.Image(shape=(180,180)) |
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label = gr.outputs.Label(num_top_classes=5) |
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gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True') |
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