Create simple_neural_network.py

Author: yashk2000

SimpleNeuralNetwork/simple_neural_network.py

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+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from keras.models import Sequential
+from keras.layers import Activation
+from keras.optimizers import SGD
+from keras.layers import Dense
+from keras.utils import np_utils
+from imutils import paths
+import numpy as np
+import argparse
+import cv2
+import os
+
+def image_to_feature_vector(image, size=(32, 32)):
+	return cv2.resize(image, size).flatten()
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-d", "--dataset", required=True,
+	help="path to input dataset")
+ap.add_argument("-m", "--model", required=True,
+	help="path to output model file")
+args = vars(ap.parse_args())
+
+print("[INFO] describing images...")
+imagePaths = list(paths.list_images(args["dataset"]))
+
+data = []
+labels = []
+
+for (i, imagePath) in enumerate(imagePaths):
+	image = cv2.imread(imagePath)
+	label = imagePath.split(os.path.sep)[-1].split(".")[0]
+
+	features = image_to_feature_vector(image)
+	data.append(features)
+	labels.append(label)
+
+	if i > 0 and i % 1000 == 0:
+		print("[INFO] processed {}/{}".format(i, len(imagePaths)))
+
+le = LabelEncoder()
+labels = le.fit_transform(labels)
+
+data = np.array(data) / 255.0
+labels = np_utils.to_categorical(labels, 2)
+
+print("[INFO] constructing training/testing split...")
+(trainData, testData, trainLabels, testLabels) = train_test_split(
+	data, labels, test_size=0.25, random_state=42)
+
+model = Sequential()
+model.add(Dense(768, input_dim=3072, init="uniform",
+	activation="relu"))
+model.add(Dense(384, activation="relu", kernel_initializer="uniform"))
+model.add(Dense(2))
+model.add(Activation("softmax"))
+
+print("[INFO] compiling model...")
+sgd = SGD(lr=0.01)
+model.compile(loss="binary_crossentropy", optimizer=sgd,
+	metrics=["accuracy"])
+model.fit(trainData, trainLabels, epochs=50, batch_size=128,
+	verbose=1)
+
+print("[INFO] evaluating on testing set...")
+(loss, accuracy) = model.evaluate(testData, testLabels,
+	batch_size=128, verbose=1)
+print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss,
+	accuracy * 100))
+
+print("[INFO] dumping architecture and weights to file...")
+model.save(args["model"])