def train():
batch_size = 128
epochs = 100
learning_rate = 0.0001
model_name = "F:/Realtime_Hand_tracking/cnn/models/hand_poses_wGarbage_" + str(epochs) + ".h5"
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
x_train, y_train, x_test, y_test = dataset.load_data(poses=["all"])
num_classes = len(np.unique(y_test))
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
# 1 - Convolution
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(28, 28, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))
# 2nd Convolution layer
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))
# 3rd Convolution layer
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))
# 4th Convolution layer
model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))
# Flattening
model.add(Flatten())
# Fully connected layer 1st layer
model.add(Dense(256))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=learning_rate),
metrics=['accuracy'])
##TRAIN##
hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=2,
validation_split=0.2)
# Evaluation
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_name)
# plotting the metrics
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.subplot(2,1,2)
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.tight_layout()
plt.show()
def train():
batch_size = 128
epochs = 10
learning_rate = 0.01
model_name = "cnn/models/hand_pos_190819_" + str(epochs) + ".h5"
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
x_train, y_train, x_test, y_test = dataset.load_data(poses=["all"])
num_classes = len(np.unique(y_test))
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
""" Model structure """
# model building
model = Sequential()
# convolutional layer with rectified linear unit activation
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
# 32 convolution filters used each of size 3x3
# again
model.add(Conv2D(64, (3, 3), activation='relu'))
# 64 convolution filters used each of size 3x3
# choose the best features via pooling
model.add(MaxPooling2D(pool_size=(2, 2)))
# randomly turn neurons on and off to improve convergence
model.add(Dropout(0.25))
# flatten since too many dimensions, we only want a classification output
model.add(Flatten())
# fully connected to get all relevant data
model.add(Dense(128, activation='relu'))
# one more dropout for convergence' sake :)
model.add(Dropout(0.5))
# output a softmax to squash the matrix into output probabilities
model.add(Dense(num_classes, activation='softmax'))
# categorical ce since we have multiple classes (10)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(lr=learning_rate),
metrics=['accuracy'])
model.summary()
""" TRAINING """
hist = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(x_test, y_test))
""" Evaluation """
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_name)
def train():
batch_size = 32
epochs = 10
learning_rate = 0.01
model_name = "F:/Github/Hand_pose_DKE/cnn/models/hand_poses_wGarbage_" + str(epochs) + ".h5"
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
x_train, y_train, x_test, y_test = dataset.load_data(poses=["all"])
num_classes = len(np.unique(y_test))
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
datagen.fit(x_train)
####### Model structure #######
#model building
# model = Sequential()
# #convolutional layer with rectified linear unit activation
# model.add(Conv2D(32, kernel_size=(3, 3),
# activation='relu',
# input_shape=input_shape))
# # 32 convolution filters used each of size 3x3
# # again
# model.add(Conv2D(64, (3, 3), activation='relu'))
# # 64 convolution filters used each of size 3x3
# # choose the best features via pooling
# model.add(MaxPooling2D(pool_size=(2, 2)))
# # randomly turn neurons on and off to improve convergence
# model.add(Dropout(0.25))
# # flatten since too many dimensions, we only want a classification output
# model.add(Flatten())
# # fully connected to get all relevant data
# model.add(Dense(128, activation='relu'))
# # one more dropout for convergence' sake :)
# model.add(Dropout(0.5))
# # output a softmax to squash the matrix into output probabilities
# model.add(Dense(num_classes, activation='softmax'))
# # categorical ce since we have multiple classes (10)
# model.compile(loss=keras.losses.categorical_crossentropy,
# optimizer=keras.optimizers.Adam(lr=learning_rate),
# metrics=['accuracy'])
#
# ####### TRAINING #######
# hist = model.fit(x_train, y_train,
# batch_size=batch_size,
# epochs=epochs,
# verbose=2,
# validation_split= 0.1)
model = Sequential()
# 1 - Convolution
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(28, 28, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# 2nd Convolution layer
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# 3rd Convolution layer
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# 4th Convolution layer
model.add(Conv2D(512, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# Flattening
model.add(Flatten())
# Fully connected layer 1st layer
model.add(Dense(256))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
# Fully connected layer 2nd layer
model.add(Dense(512))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=learning_rate),
metrics=['accuracy'])
##TRAIN##
hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=2,
validation_split=0.1)
#hist = model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),steps_per_epoch = len(x_train) / 32, epochs = epochs)
# Evaluation
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_name)
# plotting the metrics
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.subplot(2,1,2)
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.tight_layout()
plt.show()
def train():
batch_size = 128
epochs = 30
learning_rate = 0.01
model_name = "cnn/models/hand_poses_new_no_data_aug" + str(epochs) + ".h5"
# input image dimensions
img_rows, img_cols = 128, 96
# the data, shuffled and split between train and test sets
x_train, y_train, x_test, y_test = dataset.load_data(poses=["all"])
num_classes = len(np.unique(y_test))
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
####### Model structure #######
#model building
model = Sequential()
#convolutional layer with rectified linear unit activation
model.add(
Conv2D(32,
kernel_size=(5, 5),
activation='relu',
input_shape=input_shape))
# 32 convolution filters used each of size 3x3
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
# again
model.add(Conv2D(32, (3, 3), activation='relu'))
# 64 convolution filters used each of size 3x3
# model.add(Conv2D(64, (3, 3), activation='relu',padding='SAME'))
# choose the best features via pooling
model.add(MaxPooling2D(pool_size=(2, 2)))
# randomly turn neurons on and off to improve convergence
model.add(Dropout(0.20))
# flatten since too many dimensions, we only want a classification output
model.add(Flatten())
# fully connected to get all relevant data
model.add(Dense(128, activation='relu'))
# one more dropout for convergence' sake :)
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
# output a softmax to squash the matrix into output probabilities
model.add(Dense(num_classes, activation='softmax'))
# categorical ce since we have multiple classes (10)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(lr=learning_rate),
metrics=['accuracy'])
####### TRAINING #######
hist = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(x_test, y_test))
# Evaluation
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_name)
# plotting the metrics
fig = plt.figure()
plt.subplot(2, 1, 1)
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.subplot(2, 1, 2)
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.tight_layout()
plt.show()