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import pickle
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(patience=2, restore_best_weights=True)
dataDictTrain = pickle.load(open('../processed_data/trainData.pickle', 'rb'))
cleanedData = []
cleanedLabels = []
for i, item in enumerate(dataDictTrain['data']):
if isinstance(item, (np.ndarray, list)) and len(item) == 42:
cleanedData.append(np.array(item, dtype=np.float32))
cleanedLabels.append(dataDictTrain['labels'][i])
xTrain = np.array(cleanedData)
yTrainRaw = np.array(cleanedLabels)
dataDictTest = pickle.load(open('../processed_data/testData.pickle', 'rb'))
xTestRaw = np.array(dataDictTest['data'], dtype=np.float32)
yTestRaw = np.array(dataDictTest['labels'])
xTrain = xTrain.reshape(-1, 42, 1)
xTest = xTestRaw.reshape(-1, 42, 1)
labelEncoder = LabelEncoder()
labelEncoder.fit(yTrainRaw)
yTrainEncoded = labelEncoder.transform(yTrainRaw)
yTestEncoded = labelEncoder.transform(yTestRaw)
#second data set for finetuning
dataDictTrain2 = pickle.load(open('../processed_data/fineTuneData.pickle', 'rb'))
cleanedData2 = []
cleanedLabels2 = []
for i, item in enumerate(dataDictTrain2['data']):
if isinstance(item, (np.ndarray, list)) and len(item) == 42:
cleanedData2.append(np.array(item, dtype=np.float32))
cleanedLabels2.append(dataDictTrain2['labels'][i])
xTrain2 = np.stack(cleanedData2)
yTrainRaw2= np.array(cleanedLabels2)
xTrain2 = xTrain2.reshape(-1, 42, 1)
labelEncoder2 = LabelEncoder()
labelEncoder2.fit(yTrainRaw2)
yTrainEncoded2 = labelEncoder2.transform(yTrainRaw2)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv1D(32, kernel_size=3, activation='relu', input_shape=(42, 1)),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.Conv1D(64, kernel_size=3, activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(len(labelEncoder.classes_), activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(xTrain, yTrainEncoded, epochs=15, batch_size=32, validation_split=0.2)
for layer in model.layers[:-2]:
layer.trainable = False
model.compile(optimizer=tf.keras.optimizers.Adam(1e-5),loss='sparse_categorical_crossentropy',metrics=['accuracy'])
yTrainEncoded2 = labelEncoder2.transform(yTrainRaw2)
model.fit(xTrain2, yTrainEncoded2, epochs=10, batch_size=32, validation_split=0.2, callbacks = [early_stop])
testLoss, testAccuracy = model.evaluate(xTest, yTestEncoded)
print(f"\nTest accuracy: {testAccuracy * 100:.2f}%")
predictions = model.predict(xTest)
predictedIndex = np.argmax(predictions, axis=1)
predictedLabels = labelEncoder.inverse_transform(predictedIndex)
print("\nPredictions:")
for i, pred in enumerate(predictedLabels):
print(f"Image {i}: Predicted = {pred}, Actual = {yTestRaw[i]}")
model.save("detectLettersModel.keras")
with open("labelEncoder.pickle", "wb") as file:
pickle.dump(labelEncoder, file) |