Added model training .py file for signs

Training_models/Preprocessing and Training_signs.py

+# -*- coding: utf-8 -*-
+"""Untitled6.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-DVJ3LLIXgaCWW8Bb6CP-ei7O2k6tLwD
+"""
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.preprocessing.image import ImageDataGenerator
+import numpy as np
+from sklearn.model_selection import train_test_split
+import os
+from PIL import Image
+
+# Load the data
+data = []
+labels = []
+
+folder_names = ['good', 'hello', 'home', 'love', 'me', 'more', 'mother', 'no', 'phone', 'stop', 'where', 'why', 'yes']
+
+for folder_index, folder_name in enumerate(folder_names):
+    path = 'drive/MyDrive/my_dataset/{}'.format(folder_name)
+    for filename in os.listdir(path):
+        img = Image.open(os.path.join(path, filename)).convert('L')
+        img = img.resize((128, 128))
+        img = np.array(img)
+        img = img / 255
+        data.append(img)
+        labels.append(folder_index)
+
+X_train, X_test, y_train, y_test = train_test_split(data, labels, random_state=42, test_size=0.2)
+
+# Convert the data to arrays
+X_train = np.array(X_train)
+X_test = np.array(X_test)
+
+# Expand dimensions for grayscale images
+X_train = np.expand_dims(X_train, axis=3)
+X_test = np.expand_dims(X_test, axis=3)
+
+# Convert the labels to categorical
+num_classes = len(folder_names)
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+# Create the model
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(128, 128, 1)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
+
+# Data augmentation
+datagen = ImageDataGenerator(
+    rotation_range=30,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+# Define early stopping
+early_stopping = keras.callbacks.EarlyStopping(
+    monitor='val_accuracy',
+    patience=10,
+    restore_best_weights=True,
+    min_delta=0.001
+)
+
+# Fit the model with data augmentation
+batch_size = 10
+epochs = 5
+steps_per_epoch = len(X_train) // batch_size
+
+model.fit(
+    datagen.flow(X_train, y_train, batch_size=batch_size),
+    steps_per_epoch=steps_per_epoch,
+    epochs=epochs,
+    validation_data=(X_test, y_test),
+    callbacks=[early_stopping]
+)
+
+CNN_Score = model.evaluate(np.array(X_test), np.array(y_test))
+
+print("Test Loss: {:.5f}".format(CNN_Score[0]))
+print("Test Accuracy: {:.2f}%".format(CNN_Score[1] * 100))
+
+CNN_Score = model.evaluate(np.array(X_train), np.array(y_train))
+
+print("Train Loss: {:.5f}".format(CNN_Score[0]))
+print("Train Accuracy: {:.2f}%".format(CNN_Score[1] * 100))
+
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Predict labels for the test set
+y_pred = model.predict(np.array(X_test))
+y_pred = np.argmax(y_pred, axis=1)
+
+# Convert true labels to multiclass format
+y_true = np.argmax(np.array(y_test), axis=1)
+
+# Compute confusion matrix
+cm = confusion_matrix(y_true, y_pred)
+
+# Plot confusion matrix
+plt.figure(figsize=(7, 6))
+sns.heatmap(cm, annot=True, cmap="Blues", fmt="d", xticklabels=range(13), yticklabels=range(13))
+plt.xlabel('Predicted')
+plt.ylabel('True')
+plt.title('Confusion Matrix')
+plt.show()
+
+from sklearn.metrics import classification_report
+
+# Calculate and display the classification report
+report = classification_report(y_true, y_pred)
+print(report)
+
+# Save the model
+model.save("trained_model.h5")
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