def trainModel( model = None):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# Returns:
# model: trained Keras model
crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 224
datasrc = "ilsvrc14"
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_v3.AugSequence ( target_size=target_size, crop_range=crop_range, batch_size=256, datasrc=datasrc, test=False, debug=True )
if model is None:
input_shape = (target_size, target_size, 3)
model = m_v7.prepModel ( input_shape = input_shape, \
L1_size_stride_filters = (7, 2, 96), L1MaxPool_size_stride = (3, 2), \
L2_size_stride_filters = (5, 2, 256), L2MaxPool_size_stride = (3, 2), \
L3_size_stride_filters = (3, 1, 384), \
L4_size_stride_filters = (3, 1, 384), \
L5_size_stride_filters = (3, 1, 256), L5MaxPool_size_stride = (3, 2), \
D1_size = 4096, \
D2_size = 4096)
full_epochs = 50
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=full_epochs, verbose=2 )
e_v2.eval(model, target_size=target_size, datasrc=datasrc)
e_v2.eval(model, target_size=target_size, datasrc=datasrc, test=True)
return model
def trainModel(model=None):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# Returns:
# model: trained Keras model
crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 224
datasrc = "ilsvrc14"
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_v3.AugSequence(target_size=target_size,
crop_range=crop_range,
batch_size=128,
datasrc=datasrc,
test=False,
debug=True)
if model is None:
input_shape = (target_size, target_size, 3)
model = m_v7.prepModel ( input_shape = input_shape, \
L1_size_stride_filters = (7, 2, 96), L1MaxPool_size_stride = (3, 2), \
L2_size_stride_filters = (5, 2, 256), L2MaxPool_size_stride = (3, 2), \
L3_size_stride_filters = (3, 1, 384), \
L4_size_stride_filters = (3, 1, 384), \
L5_size_stride_filters = (3, 1, 256), L5MaxPool_size_stride = (3, 2), \
D1_size = 4096, \
D2_size = 4096)
full_epochs = 200
#prepare a validation data generator, used for early stopping
vldDataGen = dg_v1.prepDataGen(target_size=target_size,
test=True,
batch_size=128,
datasrc=datasrc)
callback_earlystop = EarlyStopping(monitor='val_acc',
min_delta=0.01,
patience=5,
verbose=1,
mode='max',
restore_best_weights=True)
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
model.fit_generator(dataGen,
steps_per_epoch=len(dataGen),
epochs=full_epochs,
verbose=2,
validation_data=vldDataGen,
validation_steps=len(vldDataGen),
callbacks=[callback_earlystop])
e_v3.eval(model, target_size=target_size, datasrc=datasrc)
e_v3.eval(model, target_size=target_size, datasrc=datasrc, test=True)
return model
def trainModel( model = None, epochs = 1):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# epochs - number of max epochs to train (subject to early stopping)
# Returns:
# model: trained Keras model
crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 224
datasrc = "ilsvrc14_50classes"
#datasrc = "ilsvrc14"
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_v3.AugSequence ( target_size=target_size, crop_range=crop_range, batch_size=128, datasrc=datasrc, test=False, debug=True )
if model is None:
input_shape = (target_size, target_size, 3)
model = m_v8.prepModel ( input_shape = input_shape, \
L1_size_stride_filters = (11, 4, 96), L1MaxPool_size_stride = (3, 2), L1_dropout = 0.0, \
L2_size_stride_filters = (5, 1, 256), L2MaxPool_size_stride = (3, 2), L2_dropout = 0.0, \
L3_size_stride_filters = (3, 1, 384), L3_dropout = 0.0, \
L4_size_stride_filters = (3, 1, 384), L4_dropout = 0.0, \
L5_size_stride_filters = (3, 1, 256), L5MaxPool_size_stride = (3, 2), L5_dropout = 0.0, \
D1_size = 4096, D1_dropout = 0.2, \
D2_size = 4096, D2_dropout = 0.3, \
Softmax_size = 50, \
Conv_padding = "same" )
#prepare a validation data generator, used for early stopping
vldDataGen = dg_v1.prepDataGen( target_size=target_size, test=True, batch_size=128, datasrc=datasrc )
callback_earlystop = EarlyStopping ( monitor='val_acc', min_delta=0.001, patience=20, verbose=1, mode='max', restore_best_weights=True )
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=epochs, verbose=2, validation_data=vldDataGen, validation_steps=len(vldDataGen), callbacks=[callback_earlystop] )
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2, validation_data=vldDataGen, validation_steps=len(vldDataGen), callbacks=[callback_earlystop] )
#model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=epochs, verbose=2 )
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2 )
print ("Evaluation on train set (1 frame)")
e_v2.eval(model, target_size=target_size, datasrc=datasrc)
print ("Evaluation on validation set (1 frame)")
e_v2.eval(model, target_size=target_size, datasrc=datasrc, test=True)
print ("Evaluation on validation set (5 frames)")
e_v3.eval(model, target_size=target_size, datasrc=datasrc, test=True)
print ("Evaluation on validation set (10 frames)")
e_v4.eval(model, target_size=target_size, datasrc=datasrc, test=True)
return model
def trainModel(model=None, epochs=1):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# epochs - number of max epochs to train (subject to early stopping)
# Returns:
# model: trained Keras model
crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 224
datasrc = "ilsvrc14_50classes"
#datasrc = "ilsvrc14"
# "presumed mean" of X, subtract from all input
#subtractMean=0.5
# Load pre-calculated RGB mean, PCA (Principal Component Analysis) eigenvectors and eigenvalues
#subtractMean=np.array ( [ 0.4493, 0.4542, 0.3901 ] )
subtractMean = np.load("..\\rgb_mean.npy")
pca_eigenvectors = np.load("..\\eigenvectors.npy")
pca_eigenvalues = np.load("..\\eigenvalues.npy")
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_v4.AugSequence ( target_size=target_size, crop_range=crop_range, allow_hor_flip=True, batch_size=128, \
subtractMean=subtractMean, pca_eigenvectors=pca_eigenvectors, pca_eigenvalues=pca_eigenvalues, \
datasrc=datasrc, test=False )
if model is None:
input_shape = (target_size, target_size, 3)
model = m_v8.prepModel ( input_shape = input_shape, \
L1_size_stride_filters = (11, 4, 96), L1MaxPool_size_stride = (3, 2), L1_dropout = 0.0, \
L2_size_stride_filters = (5, 1, 256), L2MaxPool_size_stride = (3, 2), L2_dropout = 0.0, \
L3_size_stride_filters = (3, 1, 384), L3_dropout = 0.0, \
L4_size_stride_filters = (3, 1, 384), L4_dropout = 0.0, \
L5_size_stride_filters = (3, 1, 256), L5MaxPool_size_stride = (3, 2), L5_dropout = 0.0, \
D1_size = 4096, D1_dropout = 0.5, \
D2_size = 4096, D2_dropout = 0.55, \
Softmax_size = 50, \
Conv_padding = "same" )
#prepare a validation data generator, used for early stopping
#vldDataGen = dg_v1.prepDataGen( target_size=target_size, test=True, batch_size=128, datasrc=datasrc )
vldDataGen = as_v3.AugSequence(target_size=target_size,
crop_range=1,
allow_hor_flip=False,
batch_size=128,
subtractMean=subtractMean,
datasrc=datasrc,
test=True)
callback_earlystop = EarlyStopping(monitor='val_acc',
min_delta=0.001,
patience=20,
verbose=1,
mode='max',
restore_best_weights=True)
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
model.fit_generator(dataGen,
steps_per_epoch=len(dataGen),
epochs=epochs,
verbose=2,
validation_data=vldDataGen,
validation_steps=len(vldDataGen),
callbacks=[callback_earlystop])
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2, validation_data=vldDataGen, validation_steps=len(vldDataGen), callbacks=[callback_earlystop] )
#model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=epochs, verbose=2 )
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2 )
print("Evaluation on train set (1 frame)")
e_v2.eval(model,
target_size=target_size,
subtractMean=subtractMean,
datasrc=datasrc)
print("Evaluation on validation set (1 frame)")
e_v2.eval(model,
target_size=target_size,
subtractMean=subtractMean,
datasrc=datasrc,
test=True)
print("Evaluation on validation set (5 frames)")
e_v3.eval(model,
target_size=target_size,
subtractMean=subtractMean,
datasrc=datasrc,
test=True)
print("Evaluation on validation set (10 frames)")
e_v4.eval(model,
target_size=target_size,
subtractMean=subtractMean,
datasrc=datasrc,
test=True)
return model
def trainModel(epochs=2, cntExperiments=2):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# epochs - number of max epochs to train (subject to early stopping)
# cntExperiments - count of experiments to run using various random dropout rates
# Returns:
# model: trained Keras model
crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 224
datasrc = "ilsvrc14_50classes"
#datasrc = "ilsvrc14"
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_v3.AugSequence(target_size=target_size,
crop_range=crop_range,
batch_size=128,
datasrc=datasrc,
test=False,
debug=True)
#prepare a validation data generator, used for early stopping
vldDataGen = dg_v1.prepDataGen(target_size=target_size,
test=True,
batch_size=128,
datasrc=datasrc)
input_shape = (target_size, target_size, 3)
# open a file for writing the results
res_file = open("train_v50.dropout.results.csv", "w")
res_file.write(
"Experiment No,cnn1,cnn2,cnn3,cnn4,cnn5,d1,d2,train accuracy,train top5,test accuracy, test top5\n"
)
for exper in range(cntExperiments):
cnn_dropout = np.array([0., 0., 0., 0., 0.])
incl_cnn_droput = np.random.rand(
) > 0.0 # 70% chance of using dropout in CNN layers
# Each dropout value has a 30% chance to be 0; then equal chance of 10%, 20%, ... 70%
if incl_cnn_droput:
cnn_dropout = np.maximum(0, np.random.randint(0, 10, 5) - 2) * 0.1
dense_dropout = np.maximum(0, np.random.randint(0, 10, 2) - 2) * 0.1
model = m_v8.prepModel ( input_shape = input_shape, \
L1_size_stride_filters = (11, 4, 96), L1MaxPool_size_stride = (3, 2), L1_dropout = cnn_dropout[0], \
L2_size_stride_filters = (5, 1, 256), L2MaxPool_size_stride = (3, 2), L2_dropout = cnn_dropout[1], \
L3_size_stride_filters = (3, 1, 384), L3_dropout = cnn_dropout[2], \
L4_size_stride_filters = (3, 1, 384), L4_dropout = cnn_dropout[3], \
L5_size_stride_filters = (3, 1, 256), L5MaxPool_size_stride = (3, 2), L5_dropout = cnn_dropout[4], \
D1_size = 4096, D1_dropout = dense_dropout[0], \
D2_size = 4096, D2_dropout = dense_dropout[1], \
Softmax_size = 50, \
Conv_padding = "same" )
#callback_earlystop = EarlyStopping ( monitor='val_acc', min_delta=0.001, patience=20, verbose=1, mode='max', restore_best_weights=True )
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
#model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=epochs, verbose=2, validation_data=vldDataGen, validation_steps=len(vldDataGen), callbacks=[callback_earlystop] )
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2, validation_data=vldDataGen, validation_steps=len(vldDataGen), callbacks=[callback_earlystop] )
model.fit_generator(dataGen,
steps_per_epoch=len(dataGen),
epochs=epochs,
verbose=2,
validation_data=vldDataGen,
validation_steps=len(vldDataGen))
#model.fit_generator ( dataGen, steps_per_epoch=len(dataGen), epochs=epochs, verbose=2 )
#model.fit_generator ( dataGen, steps_per_epoch=1, epochs=epochs, verbose=2 )
#print ("Evaluation on train set (1 frame)")
(ev_train, ev_test) = e_v1.eval(model,
target_size=target_size,
datasrc=datasrc)
res_line = str(exper) + "," + \
str(cnn_dropout[0]) + "," + \
str(cnn_dropout[1]) + "," + \
str(cnn_dropout[2]) + "," + \
str(cnn_dropout[3]) + "," + \
str(cnn_dropout[4]) + "," + \
str(dense_dropout[0]) + "," + \
str(dense_dropout[1]) + "," + \
str(ev_train[1]) + "," + \
str(ev_train[2]) + "," + \
str(ev_test[1]) + "," + \
str(ev_test[2]) + "\n"
res_file.write(res_line)
res_file.flush()
res_file.close()
#print ("Evaluation on validation set (1 frame)")
#e_v2.eval(model, target_size=target_size, datasrc=datasrc, test=True)
#print ("Evaluation on validation set (5 frames)")
#e_v3.eval(model, target_size=target_size, datasrc=datasrc, test=True)
#print ("Evaluation on validation set (10 frames)")
#e_v4.eval(model, target_size=target_size, datasrc=datasrc, test=True)
return model
# To run:
# cd C:\labs\KerasImagenetFruits\KerasTrainImagenet
# python
# exec(open("reimport.py").read())
# exec(open("PCA.py").read())
from DataGen import AugSequence_v3_randomcrops as as_v3
import numpy as np
from numpy.linalg import eig
from PIL import Image
crop_range = 1
target_size = 255
datasrc = "ilsvrc14_50classes"
dataGen = as_v3.AugSequence ( target_size=target_size, crop_range=crop_range, allow_hor_flip=True, batch_size=128, subtractMean=0.0, datasrc=datasrc, test=False )
# First go over the data set to get the RGB means
m = 0
i = 0
RGB_mean = np.array ( [ 0., 0., 0. ] )
for X,Y in dataGen:
m_batch = X.shape[0]
RGB_mean_batch = np.mean ( np.mean ( np.mean (X, axis=2), axis=1), axis=0) #mean accross all training example, height, width
#print (RGB_mean_batch.shape, RGB_mean.shape, RGB_mean_batch, RGB_mean )
RGB_mean = ( RGB_mean * m + RGB_mean_batch * m_batch ) / (m + m_batch)
m += m_batch
i +=1
print ("iter", i, "/", len(dataGen), "; RGB_mean:", RGB_mean)
print ("Final RGB_mean:", RGB_mean)