"""use python3 Chapter19/visualize_architecture.py""" from nn.conv.lenet import LeNet from keras.utils import plot_model # initialize LeNet and then write the network architecture # visualization graph to disk model = LeNet.build(28, 28, 1, 10) plot_model(model, to_file="Chapter19/lenet.png", show_shapes=True)
input_size = n*n*3
sp = SimplePreprocessor(n, n)
iap = ImageToArrayPreprocessor()
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(image_paths, verbose=500)
data = data.astype("float") / 255.0
(train_x, test_x, train_y, test_y) = train_test_split(data, labels, test_size=0.25, random_state=42)
train_y = LabelBinarizer().fit_transform(train_y)
test_y = LabelBinarizer().fit_transform(test_y)
print ("[INFO] compiling model...")
opt = SGD(lr=0.005)
model = LeNet.build(width=32, height=32, depth=3, classes=4)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
print ("[INFO] training network...")
H = model.fit(train_x, train_y, validation_data=(test_x, test_y), batch_size=32, epochs=100, verbose=1)
print("[INFO] evaluating network...")
predictions = model.predict(test_x, batch_size=32)
print(classification_report(test_y.argmax(axis=1), predictions.argmax(axis=1), target_names=["diamonds", "hearts", "spades", "three_sisters"]))
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 100), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 100), H.history["val_loss"], label="val_loss")
from keras.utils import plot_model from nn.conv.lenet import LeNet model = LeNet.build(width=28, height=28, depth=1, classes=10) plot_model(model, to_file='lenet.png', show_shapes=True)
def main():
args = option()
class_name = ['Apple','Avocado','Banana','Coconut','Custard_apple',
'Dragon_fruit','Guava','Mango','Orange','Plum',
'Start_fruit','Watermelon']
in_data = 'H5PY/train_normal_128.h5'
in_label = 'H5PY/labels_train_64_128.h5'
# import the feature vector and trained labels
h5f_data = h5py.File(in_data, 'r')
h5f_label = h5py.File(in_label, 'r')
data = h5f_data['dataset']
labels = h5f_label['dataset']
data = np.array(data)
labels = np.array(labels)
# reshape data matrix
if K.image_data_format() == "channels_first":
data = data.reshape(data.shape[0],3,128,128)
else:
data = data.reshape(data.shape[0],128,128,3)
print(data.shape)
# split training: 80%, testing: 20%
(trainX, testX, trainY, testY) = train_test_split(data, labels,
test_size=0.20, random_state=42)
# convert labels as vector
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.fit_transform(testY)
# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=0.05)
model = LeNet.build(width=128, height=128, depth=3, classes=12)
model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO]training network ...")
H = model.fit(trainX, trainY, validation_data=(testX,testY),
batch_size=32, epochs=40, verbose=1)
# save the network to disk
print("[INFO] serializing network ...")
model.save(args["model"])
# evaluate the network
print("[INFO] evaluating network...")
print("[INFO] shape feature: {}".format(data.shape))
preds = model.predict(testX)
print(classification_report(testY.argmax(axis=1),
preds.argmax(axis=1),
target_names=class_name))
# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 40), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 40), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 40), H.history["accuracy"], label="train_acc")
plt.plot(np.arange(0, 40), H.history["val_accuracy"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig(args["output"])