from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Flatten, Dense, Dropout
from keras import backend as K
import os
import pandas as pd
os.chdir('/content') #Google Colab notation
import utils
from keras.callbacks import ModelCheckpoint, EarlyStopping
#import PIL.Image
os.getcwd()
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples,nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='train', #Google Colab notation
test_dir_original='test', #Google Colab notation
target_base_dir='target base dir') #Google Colab notation
img_width, img_height = 150, 150
epochs = 2
batch_size = 20
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
test_generator = datagen.flow_from_directory(test_dir,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
bottleneck_features_test = model.predict_generator(
test_generator, len(test_generator))
np.save(open('bottleneck_features_test.npy', 'wb'),
bottleneck_features_test)
else:
print('bottleneck directory already exists')
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples,nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='D:\\Data-Science\\Distracted Driver\\imgs\\train',
test_dir_original='D:\\Data-Science\\Distracted Driver\\imgs\\test',
target_base_dir='D:\\Data-Science\\Distracted Driver\\bottle_neck_target')
model = applications.VGG16(include_top=False,
weights='imagenet',
input_shape=(img_width, img_height, 3))
bottleneck_dir = 'D:\\Data-Science\\Distracted Driver\\bottleneck_features'
preprocess = applications.vgg16.preprocess_input
save_bottlebeck_features(model, preprocess, train_dir, validation_dir,
test_dir, bottleneck_dir)
if (os.getcwd() == "D:\\Data-Science\\Distracted Driver"):
os.chdir("D:/Data-Science/Distracted Driver/bottleneck_features")
X_train = np.load(open('bottleneck_features_train.npy', 'rb'))
from keras.layers import Flatten, Dense
from keras import backend as K
#import collections
import os
import pandas as pd
os.chdir("D:/Data Science/Distracted Driver")
import utils
#Early stopping is required when systme realizes that there is no improvement after ceratin epochs
from keras.callbacks import ModelCheckpoint, EarlyStopping
#import PIL.Image
os.getcwd()
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples,nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='D:\\Data-Science\\Distracted Driver\\train',
test_dir_original='D:\\Data-Science\\Distracted Driver\\test',
target_base_dir='D:\\Data-Science\\Distracted Driver\\target base dir')
#Convert all images to standard width and height
img_width, img_height = 150, 150
epochs = 30
batch_size = 20
#Channels first is for Tensorflow for input shape
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else: #For non Tensorflow the imput shape is different
input_shape = (img_width, img_height, 3)
model = Sequential()
#CNN modedl with 32 filter and the filter size of 3X3 and stride as 1 and padding as 0
#FFNN requires Flatten, Dense
from keras.layers import Dense, Flatten
#from keras import backend as K
os.chdir('D:\\Data Science\\deeplearning\\Python scripts\\kaggle-cats vs dogs')
import utils
#Early stopping is required when system realizes that there is no improvement after ceratin epochs
from keras.callbacks import ModelCheckpoint, EarlyStopping
#Pip install pillow
#import PIL.Image+
os.getcwd()
#Prepare small/full data set by calling Utils class method
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples, nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='D:\\Data Science\\Data\\CatsVsDogs\\train',
test_dir_original='D:\\Data Science\\Data\\CatsVsDogs\\test',
target_base_dir='D:\\Data Science\\Data\\CatsVsDogs\\target base dir')
#Convert all images to standard width and height
img_width, img_height = 150, 150
epochs = 2 #30
batch_size = 20
# =============================================================================
# #Channels first for NON Tensorflow/Keras
# if K.image_data_format() == 'channels_first':
# input_shape = (3, img_width, img_height)
# else: #For Tensorflow the imput shape is different
# input_shape = (img_width, img_height, 3)
# =============================================================================
from keras.layers import Dense, Flatten
#from keras import backend as K
os.chdir(
'/Users/bharath/Desktop/Folder/Data Science/Projects/dogs-vs-cats-redux-kernels-edition'
)
import utils
#Early stopping is required when system realizes that there is no improvement after ceratin epochs
from keras.callbacks import ModelCheckpoint, EarlyStopping
#Pip install pillow
import PIL.Image
os.getcwd()
#Prepare small/full data set by calling Utils class method
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples, nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='/Users/bharath/Desktop/Folder/Data Science/Projects/dogs-vs-cats-redux-kernels-edition/train',
test_dir_original='/Users/bharath/Desktop/Folder/Data Science/Projects/dogs-vs-cats-redux-kernels-edition/test',
target_base_dir='/Users/bharath/Desktop/Folder/Data Science/Projects/dogs-vs-cats-redux-kernels-edition/target base dir')
#Convert all images to standard width and height
img_width, img_height = 150, 150
epochs = 2 #30
batch_size = 20
# =============================================================================
# #Channels first for NON Tensorflow/Keras
# if K.image_data_format() == 'channels_first':
# input_shape = (3, img_width, img_height)
# else: #For Tensorflow the imput shape is different
# input_shape = (img_width, img_height, 3)
# =============================================================================
#FFNN requires Flatten, Dense
from keras.layers import Dense, Flatten
from keras import backend as K
os.chdir('D:\Data\Deep Learning\dogs-vs-cats')
import utils
#Early stopping is required when system realizes that there is no improvement after ceratin epochs
from keras.callbacks import ModelCheckpoint, EarlyStopping
#Pip install pillow
#import PIL.Image
os.getcwd()
#Prepare small/full data set by calling Utils class method
train_dir, validation_dir, test_dir, nb_train_samples, nb_validation_samples, nb_test_samples = \
utils.preapare_full_dataset_for_flow(
train_dir_original='D:\Data\Deep Learning\dogs-vs-cats\train',
test_dir_original='D:\Data\Deep Learning\dogs-vs-cats\test',
target_base_dir='D:\Data\Deep Learning\dogs-vs-cats\target base dir')
#Convert all images to standard width and height
img_width, img_height = 150, 150
epochs = 2 #30
batch_size = 20
#Channels first for NON Tensorflow/Keras
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else: #For Tensorflow the imput shape is different
input_shape = (img_width, img_height, 3)
#Model begins here
model = Sequential()
