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model.save('traffic_classifier.h5')

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from sklearn.metrics import accuracy_score
test = pd.read_csv("gtsrb-german-traffic-sign/Test.csv")
test_labels = test['ClassId'].values
test_img_path = "../input/gtsrb-german-traffic-sign"
test_imgs = test['Path'].values
test_data = []
test_labels = []
for img in test_imgs:
im = Image.open(test_img_path + '/' + img)
im = im.resize((30,30))

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plt.figure(0)
plt.plot(history.history['accuracy'], label="Training accuracy")
plt.plot(history.history['val_accuracy'], label="val accuracy")
plt.title("Accuracy")
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.legend()
plt.figure(1)
plt.plot(history.history['loss'], label="training loss")
plt.plot(history.history['val_loss'], label="val loss")

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epochs = 15
history = model.fit(x_train, y_train, epochs=epochs, batch_size=64, validation_data=(x_test, y_test))

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model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

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model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5,5), activation="relu", input_shape=x_train.shape[1:]))
model.add(Conv2D(filters=32, kernel_size=(5,5), activation="relu"))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation="relu"))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation="relu"))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())

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from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
print("training shape: ",x_train.shape, y_train.shape)
print("testing shape: ",x_test.shape, y_test.shape)
y_train = to_categorical(y_train, 43)
y_test = to_categorical(y_test, 43)

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path = "gtsrb-german-traffic-sign/Train/0/00000_00004_00029.png"
img = Image.open(i0)
img = img.resize((30, 30))
sr = np.array(img)
plt.imshow(img)
plt.show()

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imgs_path = "GTSRB_dataset/Train"
data = []
labels = []
classes = 43
for i in range(classes):
img_path = os.path.join(imgs_path, str(i)) #0–42
for img in os.listdir(img_path):
im = Image.open(p + '/' + img)
im = im.resize((30,30))
im = np.array(im)

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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import tensorflow as tf
from keras.utils import to_categorical
from keras.layers import Conv2D, Dense, Flatten, MaxPool2D, Dropout