def scratchVGG16_Model():
data = helper.prepDataforCNN(numChannel=3, feat_norm=True)
trainX = data["trainX"]
valdX = data["valdX"]
trainY = data["trainY"]
valdY = data["valdY"]
_, row, col, channel = trainX.shape
digLen = 5 # including category 0
numDigits = 11
epochs = 50
batch_size = 64
vgg16Model = VGG16(include_top=False, weights=None)
vgg16Model.summary()
ptInput = keras.Input(shape=(row, col, channel), name='vgg16Scratch')
vgg16 = vgg16Model(ptInput)
# vgg16 = Conv2D(64,(3, 3), activation ='relu', padding='same')(input)
# vgg16 = Conv2D(64,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = MaxPooling2D(pool_size=(2, 2))(vgg16)
#
# vgg16 = Conv2D(128,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(128,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = MaxPooling2D(pool_size=(2, 2))(vgg16)
#
# vgg16 = Conv2D(256,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(256,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = MaxPooling2D(pool_size=(2, 2))(vgg16)
#
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = MaxPooling2D(pool_size=(2, 2))(vgg16)
#
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = Conv2D(512,(3, 3), activation ='relu', padding='same')(vgg16)
# vgg16 = MaxPooling2D(pool_size=(2, 2))(vgg16)
vgg16 = Flatten()(vgg16)
vgg16 = Dense(512, activation='relu')(vgg16)
vgg16 = Dense(512, activation='relu')(vgg16)
# vgg16 = Dense(1000, activation='relu')(vgg16)
vgg16 = Dropout(0.5)(vgg16)
numd_SM = Dense(digLen, activation='softmax', name='num')(vgg16)
dig1_SM = Dense(numDigits, activation='softmax', name='dig1')(vgg16)
dig2_SM = Dense(numDigits, activation='softmax', name='dig2')(vgg16)
dig3_SM = Dense(numDigits, activation='softmax', name='dig3')(vgg16)
dig4_SM = Dense(numDigits, activation='softmax', name='dig4')(vgg16)
numB_SM = Dense(2, activation='softmax', name='nC')(vgg16)
out = [numd_SM, dig1_SM, dig2_SM, dig3_SM, dig4_SM, numB_SM]
vgg16 = keras.Model(inputs=ptInput, outputs=out)
callback = []
optim = optimizers.Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=True)
checkpointer = keras.callbacks.ModelCheckpoint(
filepath='saved_models/vgg16.classifier.hdf5',
monitor='loss',
save_best_only=True,
verbose=2)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
verbose=1,
patience=3,
cooldown=0,
min_lr=0.000001)
# tb = keras.callbacks.TensorBoard(log_dir='logs', write_graph=True, write_images=True)
es = keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0.00000001,
patience=5,
verbose=1,
mode='auto')
callback.append(es)
callback.append(checkpointer)
callback.append(reduce_lr)
vgg16.summary()
vgg16.compile(loss='sparse_categorical_crossentropy',
optimizer=optim,
metrics=['accuracy'])
vgg16History = vgg16.fit(x=trainX,
y=trainY,
batch_size=batch_size,
epochs=epochs,
verbose=1,
shuffle=True,
validation_data=(valdX, valdY),
callbacks=callback)
print(vgg16History.history.keys())
modName = 'vgg16_Scratch'
print(vgg16History.history.keys())
createSaveMetricsPlot(vgg16History, modName, data, vgg16)
def preTrainedVGG16_Model():
data = helper.prepDataforCNN(numChannel=3, feat_norm=True)
trainX = data["trainX"]
valdX = data["valdX"]
trainY = data["trainY"]
valdY = data["valdY"]
_, row, col, channel = trainX.shape
digLen = 5
numDigits = 11
epochs = 50
batch_size = 64
optim = optimizers.Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=True)
preTrainModel = VGG16(include_top=False, weights='imagenet')
preTrainModel.summary()
ptInput = keras.Input(shape=(row, col, channel), name='inputVGGPreTrain')
pt_vgg16 = preTrainModel(ptInput)
Mout = Flatten(name='flatten')(pt_vgg16)
Mout = Dense(1024, activation='relu', name='FC1_4096')(Mout)
Mout = Dense(1024, activation='relu', name='FC1_512')(Mout)
# Mout = Dense(512, activation='relu', name = 'FC2_1024')(Mout)
# Mout = Dropout(0.5)(Mout)
numd_SM = Dense(digLen, activation='softmax', name='num')(Mout)
dig1_SM = Dense(numDigits, activation='softmax', name='dig1')(Mout)
dig2_SM = Dense(numDigits, activation='softmax', name='dig2')(Mout)
dig3_SM = Dense(numDigits, activation='softmax', name='dig3')(Mout)
dig4_SM = Dense(numDigits, activation='softmax', name='dig4')(Mout)
numB_SM = Dense(2, activation='softmax', name='nC')(Mout)
out = [numd_SM, dig1_SM, dig2_SM, dig3_SM, dig4_SM, numB_SM] #numd_SM
vggPreTrain = keras.Model(inputs=ptInput, outputs=out)
vggPreTrain.compile(
loss='sparse_categorical_crossentropy', #ceLoss ,
optimizer=optim,
metrics=['accuracy']) #[])
vggPreTrain.summary()
callback = []
checkpointer = keras.callbacks.ModelCheckpoint(
filepath='saved_models/VGGPreTrained.classifier.hdf5',
monitor='loss',
save_best_only=True,
verbose=2)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
verbose=1,
patience=4,
cooldown=1,
min_lr=0.0001)
es = keras.callbacks.EarlyStopping(monitor='loss',
min_delta=0.000001,
patience=5,
verbose=1,
mode='auto')
callback.append(es)
callback.append(checkpointer)
callback.append(reduce_lr)
vggHistory = vggPreTrain.fit(x=trainX,
y=trainY,
batch_size=batch_size,
epochs=epochs,
verbose=1,
shuffle=True,
validation_data=(valdX, valdY),
callbacks=callback)
print(vggHistory.history.keys())
modName = 'vgg16_PreTrain'
# list all data in history
print(vggHistory.history.keys())
createSaveMetricsPlot(vggHistory, modName, data, vggPreTrain)
def designedCNN_Model():
data = helper.prepDataforCNN(numChannel=3, feat_norm=True)
trainX = data["trainX"]
valdX = data["valdX"]
trainY = data["trainY"]
valdY = data["valdY"]
_, row, col, channel = trainX.shape
digLen = 5 # including category 0
numDigits = 11
epochs = 75
batch_size = 64
optim = optimizers.Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=True)
# optim = optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.Session(config=config)
input = keras.Input(shape=(row, col, channel), name='customModel')
M = Conv2D(16, (3, 3), activation='relu', padding='same',
name='conv_16_1')(input)
M = Conv2D(16, (3, 3), activation='relu', padding='same',
name='conv_16_2')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2))(M)
M = Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='conv2_32_01')(M)
M = Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='conv2_32_02')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2))(M)
M = Dropout(0.5)(M)
M = Conv2D(48, (3, 3),
activation='relu',
padding='same',
name='conv2_48_01')(M)
M = Conv2D(48, (3, 3),
activation='relu',
padding='same',
name='conv2_48_02')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2))(M)
M = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='conv2_64_1')(M)
M = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='conv2_64_2')(M)
M = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='conv2_64_3')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D((2, 2), strides=1)(M)
M = Conv2D(128,
kernel_size=(5, 5),
activation='relu',
padding='same',
name='conv2_128_1')(M)
M = Conv2D(128,
kernel_size=(5, 5),
activation='relu',
padding='same',
name='conv2_128_2')(M)
M = Conv2D(128,
kernel_size=(5, 5),
activation='relu',
padding='same',
name='conv2_128_3')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2), strides=1)(M)
M = Conv2D(256,
kernel_size=(5, 5),
activation='relu',
padding='same',
name='conv2_128_5')(M)
M = Conv2D(256,
kernel_size=(5, 5),
activation='relu',
padding='same',
name='conv2_128_6')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2), strides=1)(M)
M = Dropout(0.5)(M)
M = Conv2D(256, (5, 5),
activation='relu',
padding='same',
name='conv256_1')(M)
M = Conv2D(256, (5, 5),
activation='relu',
padding='same',
name='conv256_2')(M)
M = Conv2D(256, (5, 5),
activation='relu',
padding='same',
name='conv256_3')(M)
# kernel_regularizer=regularizers.l2(0.01),
# activity_regularizer=regularizers.l1(0.01))(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D((2, 2), strides=1)(M)
M = Conv2D(512, (5, 5),
activation='relu',
padding='same',
name='conv2_512_1')(M)
M = Conv2D(512, (5, 5),
activation='relu',
padding='same',
name='conv2_512_2')(M)
M = BatchNormalization(axis=-1)(M)
M = MaxPooling2D(pool_size=(2, 2), strides=1)(M)
M = Dropout(0.25)(M)
# M = keras.layers.BatchNormalization(axis=-1)(M)
Mout = Flatten()(M)
Mout = Dense(2048, activation='relu', name='FC1_2048')(Mout)
Mout = Dense(1024, activation='relu', name='FC1_1024')(Mout)
Mout = Dense(1024, activation='relu', name='FC2_1024')(Mout)
# Mout = Dropout(0.5)(Mout)
numd_SM = Dense(digLen, activation='softmax', name='num')(Mout)
dig1_SM = Dense(numDigits, activation='softmax', name='dig1')(Mout)
dig2_SM = Dense(numDigits, activation='softmax', name='dig2')(Mout)
dig3_SM = Dense(numDigits, activation='softmax', name='dig3')(Mout)
dig4_SM = Dense(numDigits, activation='softmax', name='dig4')(Mout)
numB_SM = Dense(2, activation='softmax', name='nC')(Mout)
out = [numd_SM, dig1_SM, dig2_SM, dig3_SM, dig4_SM, numB_SM]
svhnModel = keras.Model(inputs=input, outputs=out)
lr_metric = get_lr_metric(optim)
svhnModel.compile(
loss='sparse_categorical_crossentropy', #ceLoss ,
optimizer=optim,
metrics=['accuracy']) #[])
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
verbose=1,
patience=2,
cooldown=1,
min_lr=0.00001)
svhnModel.summary()
callback = []
checkpointer = keras.callbacks.ModelCheckpoint(
filepath='saved_models/designedBGRClassifier.hdf5',
monitor='loss',
save_best_only=True,
verbose=2)
tb = keras.callbacks.TensorBoard(log_dir='logs',
write_graph=True,
batch_size=batch_size,
write_images=True)
es = keras.callbacks.EarlyStopping(
monitor='loss', #'dig1_loss',
min_delta=0.000001,
patience=5,
verbose=1,
mode='auto')
callback.append(tb)
callback.append(es)
callback.append(checkpointer)
callback.append(reduce_lr)
# svhnModel.fit_generator(
# datagen.flow(ctrain, ctrlab, batch_size=batch_size),
# batch_size = batch_size,
# epochs=epochs,
# verbose=1,
# shuffle = True,
# validation_data=(cvald, cvlab),
# callbacks= callback)
# fits the model on batches with real-time data augmentation:
# svhnModel.fit_generator(datagen.flow(ctrain, ctrlab, batch_size=batch_size),
# steps_per_epoch=len(ctrain) / batch_size,
# epochs=epochs,
# verbose=1,
# validation_data = (cvald, cvlab),
# callbacks= callback)
#
designHist = svhnModel.fit(x=trainX,
y=trainY,
batch_size=batch_size,
epochs=epochs,
verbose=1,
shuffle=True,
validation_data=(valdX, valdY),
callbacks=callback)
print(designHist.history.keys())
modName = 'customDesign'
print(designHist.history.keys())
createSaveMetricsPlot(designHist, modName, data, svhnModel)