Spaces:
Sleeping
Sleeping
File size: 2,080 Bytes
f968273 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import pickle
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
dataDictTrain = pickle.load(open('../processed_data/numTrainData.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/numTestData.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)
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)
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("detectNumbersModel.keras")
with open("numLabelEncoder.pickle", "wb") as file:
pickle.dump(labelEncoder, file) |