Upload cat&dog.py with huggingface_hub

cat&dog.py

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+# -*- coding: utf-8 -*-
+"""Cat&Dogs.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17AFfKN67SFvxF7FdjjugeJGIa000SGU8
+"""
+
+import tensorflow as tf
+
+#load the datasets
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+
+#pre-process the data 
+x_train = tf.keras.utils.normalize(x_train, axis=1) 
+x_test = tf.keras.utils.normalize(x_test, axis=1)
+
+#define the model input and set the layers
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))
+model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))
+model.add(tf.keras.layers.Dense(10, activation=tf.nn.sigmoid))
+
+#compile the model
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+#train the model
+model.fit(x_train, y_train, epochs=100)
+
+#evaluate the model
+val_loss, val_acc = model.evaluate(x_test, y_test)
+print(val_loss)
+print(val_acc)
+
+#make predictions
+predictions = model.predict(x_test)
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Select 5 random images from the test set
+indices = np.random.randint(0, len(x_test), size=1)
+images = x_test[indices]
+
+# Make predictions for the selected images
+predictions = model.predict(images)
+
+# Iterate over the images and predictions
+for i, (image, prediction) in enumerate(zip(images, predictions)):
+  # Convert the image to uint8 and reshape it to (32, 32, 3)
+  image = np.uint8(image * 255).reshape(32, 32, 3)
+
+  # Get the class label and probability
+  label = np.argmax(prediction)
+  probability = prediction[label]
+
+  # Plot the image and the prediction
+  plt.subplot(1, 5, i + 1)
+  plt.imshow(image)
+
+  
+# The labels of the CIFAR-10 dataset are represented as integers in the range 0 to 9. Each integer corresponds to a class of image:
+
+# 0: airplane
+# 1: automobile
+# 2: bird
+# 3: cat
+# 4: deer
+# 5: dog
+# 6: frog
+# 7: horse
+# 8: ship
+# 9: truck
+
+  plt.title("Prediction: {} ({:.2f})".format(label, probability))
+
+plt.show()
+
+from huggingface_hub import notebook_login
+notebook_login()
+
+from huggingface_hub import create_repo
+create_repo(repo_id="test-model")
+'https://huggingface.co/zegoop/myModel1'
+
+from huggingface_hub import upload_file
+upload_file(
+   path_or_fileobj="Cat&Dogs.ipynb", 
+   path_in_repo="cat&dog.ipynb", 
+   repo_id="zegoop/test-model"
+)
+'https://huggingface.co/zegoop/myModel1/blob/main/cat&dog.ipynb'