def demo_cnn():
X,y,w = load2d()
y = flatten_except_1dim(y,ndim=3)
w = flatten_except_1dim(w,ndim=2)
print(X.shape)
print(y.shape)
print(w.shape)
model = CNN()
hist = model.fit(X, y, sample_weight=w, epochs=EPOCH,batch_size=128, validation_split=0.2)
# plot_loss(hist.history,"CNN model",plt)
# plt.legend()
# plt.grid()
# plt.yscale("log")
# plt.xlabel("epoch")
# plt.ylabel("loss")
# plt.show()
model.save('./models/cnn_weighted_model.h5')
X_test,_,_ = load2d(test=True)
# y_test = model.predict(X_test)
# fig = plt.figure(figsize=(10, 7))
# fig.subplots_adjust(
# left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# for i in range(16):
# axis = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[])
# plot_sample(X_test[i], y_test[i], axis)
# plt.show()
df_y_pred = predict_single(model, X_test)
prepare_submission(df_y_pred,"cnn_weighted")
def train_CNN(data_portion=X.shape[0], epochs=20, ensemble=False):
model = CNN(im_shape)
# epochs = 50
batch = 100
if augment:
history = model.fit_generator(datagen.flow(train_X, train_y),
epochs=epochs,
steps_per_epoch=data_portion // 32,
verbose=2,
validation_data=[val_X, val_y],
callbacks=[annealer])
else:
history = model.fit(train_X[:data_portion],
train_y[:data_portion],
batch,
epochs,
verbose=2,
validation_split=0.3,
callbacks=[annealer])
# print(model.summary())
if not ensemble:
# print('saving')
# model.save('saved_models/my_model.h5')
# print('done')
fig = plt.figure(figsize=(15, 5))
plt.plot(history.history['val_acc'])
plt.xlabel('epoch')
plt.ylabel('val_accuracy')
plt.xlim(0, epochs)
plt.grid()
plt.ylim(.97, 1)
plt.title('Max val_acc: ' + str(max(history.history['val_acc'])))
fig.savefig('plots/testing.png')
return model
print('Start training...')
if args.model != 'DNN':
model.fit_generator(datagen.flow(train_x,
train_y,
batch_size=args.batch),
steps_per_epoch=5 * num_batches,
epochs=args.epoch,
verbose=1,
validation_data=(valid_x, valid_y),
workers=8,
callbacks=[checkpoint, earlystopping, csvlogger])
else:
model.fit(train_x,
train_y,
batch_size=args.batch,
epochs=args.epoch,
verbose=1,
validation_data=(valid_x, valid_y),
shuffle=True,
callbacks=[checkpoint, earlystopping, csvlogger])
if args.test == True:
print('\nLoad model parameters...\n')
model.load_weights(paramfile)
print('Start testing...\n')
pred = model.predict(test_x)
pred = pred.argmax(axis=-1)
print('Save prediction...\n')
outfile = open(args.output_file, 'w')
print('id,label', file=outfile)
sgd = Adam(lr=0.0001)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
callback = [
# EarlyStopping(monitor='val_loss', patience=50, verbose=True),
# ReduceLROnPlateau(monitor='lr', factor=0.1, patience=15, verbose=True),
ModelCheckpoint('./models/cnn_best_weights.h5',
monitor='val_accuracy',
verbose=True,
save_best_only=True,
save_weights_only=True)
]
history_jaffe = model.fit(train_generator,
steps_per_epoch=len(y_train) // opt.batch_size,
epochs=opt.epochs,
validation_data=valid_generator,
validation_steps=len(y_valid) // opt.batch_size,
callbacks=callback)
his = history_jaffe
else:
expr, x, y = CK().gen_train()
y = to_categorical(y).reshape(y.shape[0], -1)
# 划分训练集验证集
x_train, x_valid, y_train, y_valid = train_test_split(x,
y,
test_size=0.2,
random_state=2019)
print(
"load CK+ dataset successfully, it has {} train images and {} valid iamges"
.format(y_train.shape[0], y_valid.shape[0]))
train_generator = ImageDataGenerator(rotation_range=10,
from model import CNN
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import pandas as pd
import numpy as np
params = {'epoch': 100, 'batch_size': 32}
models = CNN(**params)
train = pd.read_csv('../data/payload_train.csv')
val = pd.read_csv('../data/payload_val.csv')
y_train = train['label'].values
x_train = train.drop('label', axis=1).values / 256
x_train = np.reshape(x_train, [-1, 64, 64, 1])
y_val = val['label'].values
x_val = val.drop('label', axis=1).values / 256
x_val = np.reshape(x_val, [-1, 64, 64, 1])
models.fit(x_train, y_train, x_val, y_val)
