
from keras.models import *
from keras.layers import *
from keras.activations import *
from keras.losses import *
from keras.optimizers import *
from keras.metrics import *
from keras.callbacks import *

import pickle

x_train=pickle.load(open('x_train.txt','rb'))
x_test=pickle.load(open('x_test.txt','rb'))
y_train=pickle.load(open('y_train.txt','rb'))
y_test=pickle.load(open('y_test.txt','rb'))

tb_callback = TensorBoard("C:/Project/kerasTest/Logs/catsdogsregressionreel/")

model = Sequential()
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(1, activation=sigmoid))
model.compile(sgd(), loss=mse, metrics=['accuracy'])

model.fit(x_train, y_train, 250, 4000, 1, [tb_callback], 0., (x_test, y_test), True, None, None, 0)
974f5f026b0c39bb0f126e9b1269e39b688ee37b
<mask token>

<mask token>
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(1, activation=sigmoid))
model.compile(sgd(), loss=mse, metrics=['accuracy'])
model.fit(x_train, y_train, 250, 4000, 1, [tb_callback], 0.0, (x_test,
    y_test), True, None, None, 0)

<mask token>
x_train = pickle.load(open('x_train.txt', 'rb'))
x_test = pickle.load(open('x_test.txt', 'rb'))
y_train = pickle.load(open('y_train.txt', 'rb'))
y_test = pickle.load(open('y_test.txt', 'rb'))
tb_callback = TensorBoard('C:/Project/kerasTest/Logs/catsdogsregressionreel/')
model = Sequential()
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(1, activation=sigmoid))
model.compile(sgd(), loss=mse, metrics=['accuracy'])
model.fit(x_train, y_train, 250, 4000, 1, [tb_callback], 0.0, (x_test,
    y_test), True, None, None, 0)

from keras.models import *
from keras.layers import *
from keras.activations import *
from keras.losses import *
from keras.optimizers import *
from keras.metrics import *
from keras.callbacks import *
import pickle
x_train = pickle.load(open('x_train.txt', 'rb'))
x_test = pickle.load(open('x_test.txt', 'rb'))
y_train = pickle.load(open('y_train.txt', 'rb'))
y_test = pickle.load(open('y_test.txt', 'rb'))
tb_callback = TensorBoard('C:/Project/kerasTest/Logs/catsdogsregressionreel/')
model = Sequential()
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(1, activation=sigmoid))
model.compile(sgd(), loss=mse, metrics=['accuracy'])
model.fit(x_train, y_train, 250, 4000, 1, [tb_callback], 0.0, (x_test,
    y_test), True, None, None, 0)


from keras.models import *
from keras.layers import *
from keras.activations import *
from keras.losses import *
from keras.optimizers import *
from keras.metrics import *
from keras.callbacks import *

import pickle

x_train=pickle.load(open('x_train.txt','rb'))
x_test=pickle.load(open('x_test.txt','rb'))
y_train=pickle.load(open('y_train.txt','rb'))
y_test=pickle.load(open('y_test.txt','rb'))

tb_callback = TensorBoard("C:/Project/kerasTest/Logs/catsdogsregressionreel/")

model = Sequential()
model.add(Flatten(input_shape=(32, 32, 3)))
model.add(Dense(1, activation=sigmoid))
model.compile(sgd(), loss=mse, metrics=['accuracy'])

model.fit(x_train, y_train, 250, 4000, 1, [tb_callback], 0., (x_test, y_test), True, None, None, 0)