CODEWITHSUNDEEP
pythontensorflowneural-networkkeras
Matheus Maia
Matheus Maia

Reputation: 15

Make a simple KERAS network for classification

I'm trying to do a simple Keras Neural Network but the model doesn't fit:

Train on 562 samples, validate on 188 samples
Epoch 1/20
562/562 [==============================] - 1s 1ms/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213
Epoch 2/20
562/562 [==============================] - 0s 298us/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213
Epoch 3/20
562/562 [==============================] - 0s 295us/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213
Epoch 4/20
562/562 [==============================] - 0s 282us/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213
Epoch 5/20
562/562 [==============================] - 0s 289us/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213
Epoch 6/20
562/562 [==============================] - 0s 265us/step - loss: 8.1130 - acc: 0.4911 - val_loss: 7.6320 - val_acc: 0.5213

The data base is structured in a CSV file like this:

doc venda   img1    img2    v1                  v2                  gt
RG  venda1  img123  img12   [3399, 162675, ...] [3399, 162675, ...] 1

My intent its to use the diff between v1 and v2 vector to answer me if img1 and im2 are from the same class.

The code:

from sklearn.model_selection import train_test_split
(X_train, X_test, Y_train, Y_test) = train_test_split(train, train_labels, test_size=0.25, random_state=42)
# create the model
model = Sequential()
model.add(Dense(10, activation="relu", input_dim=10, kernel_initializer="uniform"))
model.add(Dense(6, activation="relu", kernel_initializer="uniform"))
model.add(Dense(1, activation='sigmoid'))
print(model.summary())

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])
model.fit(
        np.array(X_train), 
        np.array(Y_train), 
        shuffle=True,
        epochs=20, 
        verbose=1,
        batch_size=5,
        validation_data=(np.array(X_test), np.array(Y_test)),
)

What i'm doing wrong?

Upvotes: 0

Views: 64

Answers (2)

MikeDoho
MikeDoho

Reputation: 552

I have had success normalizing features using this function. I forget exactly why I use the same mu and sigma from train set on the test and val but I am pretty sure I learned it during the deep.ai course on coursera

def normalize_features(dataset):
    mu = np.mean(dataset, axis = 0) # columns
    sigma = np.std(dataset, axis = 0)
    norm_parameters = {'mu': mu,
                'sigma': sigma}
    return (dataset-mu)/(sigma+1e-10), norm_parameters

# Normal X data; using same mu and sigma from test set;

x_train, norm_parameters = normalize_features(x_train)

x_val = (x_val-norm_parameters['mu'])/(norm_parameters['sigma']+1e-10)

x_test = (x_test-norm_parameters['mu'])/(norm_parameters['sigma']+1e-10)

Upvotes: 0

Pranjal Sahu
Pranjal Sahu

Reputation: 1469

Divide the difference vector by some constant number so that the feature vector is in range 0 to 1 or -1 to 1. Right now the values are too big and the loss is coming high. Network learns faster if the data is normalized properly.

Upvotes: 2

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