agnesmm

from keras import backend as K
from keras.applications.vgg16 import VGG16
from keras.layers import GlobalAveragePooling2D, Flatten, Dense, Input, Dropout
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator

#setup
path = 'data/dogscats_redux/'
#path = 'data/dogscats_redux/sample/'

agnesmm

# we initialize the model
model = Sequential()

# Conv Block 1
model.add(Conv2D(64, (3, 3), input_shape=(224,224,3), activation='relu', padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

# Conv Block 2
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))

agnesmm

import pandas as pd
import numpy as np
from collections import Counter
import itertools
import re
from keras.layers import MaxPooling1D, Conv1D, BatchNormalization
from keras.layers import Flatten, Dense, Embedding, Dropout, Dense, SpatialDropout1D
from keras.models import Sequential

agnesmm

from keras import backend as K
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Flatten, Lambda, Conv2D
from keras.layers import GlobalAveragePooling2D, Input, Dropout
from keras.layers.convolutional import MaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam, RMSprop, SGD
import numpy as np
import urllib

agnesmm

from keras import backend as K
from keras.applications.vgg16 import VGG16
from keras.layers import GlobalAveragePooling2D, Flatten, Dense, Input, Dropout
from keras.models import Model, load_model
from keras.optimizers import Adam, RMSprop, SGD
from keras.preprocessing.image import ImageDataGenerator

path = 'data/dogscats_redux/sample/'
#path = 'data/dogscats_redux/'
target_size=(224, 224)