CODEWITHSUNDEEP
tensorflowkeras
DQ_happy
DQ_happy

Reputation: 515

Use loss in the keras model function

I am trying to build the a very simple model using keras using the Model function, like below, where the input and output of the Model function are [img,labels] and the loss. I am confused why this code is not working, if the output cannot be the loss. How should the Model function work and when should we use the Model function? Thanks.

sess = tf.Session()
K.set_session(sess)
K.set_learning_phase(1)
img = Input((784,),name='img')
labels = Input((10,),name='labels')
# img = tf.placeholder(tf.float32, shape=(None, 784))
# labels = tf.placeholder(tf.float32, shape=(None, 10))

x = Dense(128, activation='relu')(img)
x = Dropout(0.5)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
preds = Dense(10, activation='softmax')(x)

from keras.losses import binary_crossentropy
#loss = tf.reduce_mean(categorical_crossentropy(labels, preds))
loss = binary_crossentropy(labels, preds)
print(type(loss))
model = Model([img,labels], loss, name='squeezenet')
model.summary()

Upvotes: 0

Views: 502

Answers (1)

Julio Daniel Reyes
Julio Daniel Reyes

Reputation: 6365

As @yu-yang pointed out, the loss is specified with compile(). If you think about it, it makes sense because the real output of your model is your prediction, not the loss, the loss is only used to train the model.

A working example of your network:

import keras
from keras.optimizers import Adam
from keras.models import Model
from keras.layers import Input, Dense, Dropout
from keras.losses import categorical_crossentropy

img = Input((784,),name='img')

x = Dense(128, activation='relu')(img)
x = Dropout(0.5)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
preds = Dense(10, activation='softmax')(x)

model = Model(inputs=img, outputs=preds, name='squeezenet')


model.compile(optimizer=Adam(),
              loss=categorical_crossentropy,
              metrics=['acc'])

model.summary()

Output:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
img (InputLayer)             (None, 784)               0         
_________________________________________________________________
dense_32 (Dense)             (None, 128)               100480    
_________________________________________________________________
dropout_21 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_33 (Dense)             (None, 128)               16512     
_________________________________________________________________
dropout_22 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_34 (Dense)             (None, 10)                1290      
=================================================================
Total params: 118,282
Trainable params: 118,282
Non-trainable params: 0
_________________________________________________________________

With MNIST dataset:

from keras.datasets import mnist
from keras.utils import to_categorical

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(-1, 784)
y_train = to_categorical(y_train, num_classes=10)
x_test = x_test.reshape(-1, 784)
y_test = to_categorical(y_test, num_classes=10)

model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

Output:

Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 4s - loss: 12.2797 - acc: 0.2360 - val_loss: 11.0902 - val_acc: 0.3116
Epoch 2/10
60000/60000 [==============================] - 4s - loss: 10.4161 - acc: 0.3527 - val_loss: 8.7122 - val_acc: 0.4589
Epoch 3/10
60000/60000 [==============================] - 4s - loss: 9.5797 - acc: 0.4051 - val_loss: 8.9226 - val_acc: 0.4460
Epoch 4/10
60000/60000 [==============================] - 4s - loss: 9.2017 - acc: 0.4285 - val_loss: 8.0564 - val_acc: 0.4998
Epoch 5/10
60000/60000 [==============================] - 4s - loss: 8.8558 - acc: 0.4501 - val_loss: 8.0878 - val_acc: 0.4980
Epoch 6/10
60000/60000 [==============================] - 5s - loss: 8.8239 - acc: 0.4521 - val_loss: 8.2495 - val_acc: 0.4880
Epoch 7/10
60000/60000 [==============================] - 4s - loss: 8.7842 - acc: 0.4547 - val_loss: 7.7146 - val_acc: 0.5211
Epoch 8/10
60000/60000 [==============================] - 4s - loss: 8.7395 - acc: 0.4575 - val_loss: 7.7944 - val_acc: 0.5163
Epoch 9/10
60000/60000 [==============================] - 5s - loss: 8.7109 - acc: 0.4593 - val_loss: 7.8235 - val_acc: 0.5145
Epoch 10/10
60000/60000 [==============================] - 4s - loss: 8.4927 - acc: 0.4729 - val_loss: 7.5933 - val_acc: 0.5288

Upvotes: 3

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