feat: add code, model and evaluation files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+my_model.keras filter=lfs diff=lfs merge=lfs -text

lstm_mnist.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+
+# Load the MNIST dataset
+(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
+
+
+# In[2]:
+
+
+X_train = train_images.astype('float32') / 255.0
+X_test = test_images.astype('float32') / 255.0
+
+
+# In[3]:
+
+
+y_train = train_labels
+y_test = test_labels
+
+
+# In[4]:
+
+
+import keras
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+import tensorflow.keras as keras  # Import Keras from TensorFlow
+from tensorflow.keras.optimizers import Adam  # Import the Adam optimizer
+
+model = keras.Sequential()
+model.add(keras.layers.Reshape((28, 28), input_shape=(28, 28)))
+model.add(keras.layers.LSTM(128, return_sequences=True))
+model.add(keras.layers.Dropout(0.2))
+model.add(keras.layers.LSTM(128))
+model.add(keras.layers.Dropout(0.2))
+model.add(keras.layers.Dense(64, activation='relu'))
+model.add(keras.layers.Dropout(0.2))
+model.add(keras.layers.Dense(10, activation='softmax'))
+
+# Define a learning rate schedule
+lr_schedule = keras.optimizers.schedules.ExponentialDecay(
+    initial_learning_rate=0.001,
+    decay_steps=10000,
+    decay_rate=0.9
+)
+
+# Create an optimizer with the learning rate schedule
+optimizer = Adam(learning_rate=lr_schedule)
+
+# Compile the model
+model.compile(loss='sparse_categorical_crossentropy',
+              optimizer=optimizer,
+              metrics=['accuracy'])
+
+# Print model summary
+model.summary()
+
+# Train the model
+history = model.fit(X_train, y_train, epochs=3, validation_data=(X_test, y_test))
+
+
+# In[5]:
+
+
+import matplotlib.pyplot as plt
+
+def predict_number(input_image):
+    # Preprocess the input image (assuming it's a grayscale image)
+    input_image = input_image.astype('float32') / 255.0
+    input_image = np.reshape(input_image, (1, 28, 28))  # Reshape to match the model's input shape
+
+    prediction = model.predict(input_image)
+
+    predicted_number = np.argmax(prediction)
+
+    return predicted_number
+
+input_image = test_images[0]  # Replace with your own image
+predicted_number = predict_number(input_image)
+
+plt.imshow(input_image.reshape((28, 28)), cmap=plt.cm.binary)
+plt.title(f"Predicted Number: {predicted_number}")
+plt.show()
+
+
+print("Predicted Number:", predicted_number)
+
+
+# In[6]:
+
+
+# Save the trained model to the current working directory
+model.save('my_model.keras')
+
+
+# In[9]:
+
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# Load your trained model (replace 'my_model.h5' with your model's filename)
+model = load_model('my_model.keras')
+
+# Define a function to predict the number from an input image file
+def predict_number_from_image(image_path):
+    # Load and preprocess the input image
+    img = image.load_img(image_path, target_size=(28, 28), color_mode='grayscale')
+    img_array = image.img_to_array(img)
+    img_array /= 255.0
+    img_array = np.reshape(img_array, (1, 28, 28, 1))  # Reshape to match the model's input shape
+
+    # Make a prediction using the trained model
+    prediction = model.predict(img_array)
+
+    # Get the index of the class with the highest probability
+    predicted_number = np.argmax(prediction)
+
+    return predicted_number
+
+# Example usage:
+# Provide an input image (as a file path)
+# Replace 'your_image_path.png' with the path to your image file
+input_image_path = 'seven.png'
+
+predicted_number = predict_number_from_image(input_image_path)
+
+# Load and display the input image
+img = Image.open(input_image_path)
+plt.imshow(img, cmap='gray')
+plt.title(f"Predicted Number: {predicted_number}")
+plt.show()
+
+# Print the predicted number
+print("Predicted Number:", predicted_number)
+

my_model.keras

@@ -0,0 +1,3 @@
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+oid sha256:76ba241eda4f72bc0293d51b74f159edd9d98d1b03a60ab45b59aede46171e27
+size 2693165