Calculate loss in Keras without running the model

Question

Is there a way to get the loss of the model, with it's current weights, without running evaluate, or fit, on it?

model = keras.Sequential([
    keras.layers.Input(400),
    keras.layers.Dense(25, activation=tf.nn.sigmoid, kernel_regularizer=regularizers.l2(lambd)),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',  # does the 1-hot encoding for us
              metrics=['accuracy'])
model.set_weights([Theta1.T, np.zeros(25), Theta2.T, np.zeros(10)])
prob = model.predict(X)
pred = np.argmax(prob, axis=1).reshape(-1, 1)
pred_y = pred == y
print(f'Training Set Accuracy: {np.mean(pred_y)*100:.2f}%') 
# How do I get the loss now?

This didn't work for me.

Answer 1

You can, via passing the outputs of model.predict(x) to an implementation of the loss function. In addition, you'll need a function to compute model regularization losses - reg_loss(model). Below is an implementation of binary_crossentropy, and l1, l2, and l1_l2 losses from all layers, including recurrent - but does not include activity_regularizer losses, which aren't weight losses. You can replace binary_crossentropy with your own function - e.g. sparse_softmax_crossentropy_with_logits:

---

WORKING IMPLEMENTATION: (numerically stable version)

def binary_crossentropy(y_true, y_pred, sample_weight=1):
    if len(y_pred.shape)==1:
        y_pred = np.atleast_2d(y_pred).T
    y_pred = [max(min(pred[0], 1-K.epsilon()), K.epsilon()) for pred in y_pred]
    y_true,y_pred,sample_weight = force_2d_shape([y_true,y_pred,sample_weight])

    logits = np.log(y_pred) - np.log(1-y_pred) # sigmoid inverse
    neg_abs_logits = -np.abs(logits)
    relu_logits    = (logits > 0)*logits

    loss_vec = relu_logits - logits*y_true + np.log(1 + np.exp(neg_abs_logits))
    return np.mean(sample_weight*loss_vec)

def force_2d_shape(arr_list):
    for arr_idx, arr in enumerate(arr_list):
        if len(np.array(arr).shape) != 2:
            arr_list[arr_idx] = np.atleast_2d(arr).T
    return arr_list

def l1l2_weight_loss(model):
    l1l2_loss = 0
    for layer in model.layers:
        if 'layer' in layer.__dict__ or 'cell' in layer.__dict__:
            l1l2_loss += _l1l2_rnn_loss(layer)
            continue

        if 'kernel_regularizer' in layer.__dict__ or \
           'bias_regularizer'   in layer.__dict__:
            l1l2_lambda_k, l1l2_lambda_b = [0,0], [0,0] # defaults
            if layer.__dict__['kernel_regularizer'] is not None:
                l1l2_lambda_k = list(layer.kernel_regularizer.__dict__.values())
            if layer.__dict__['bias_regularizer']   is not None:
                l1l2_lambda_b = list(layer.bias_regularizer.__dict__.values())

            if any([(_lambda != 0) for _lambda in (l1l2_lambda_k + l1l2_lambda_b)]):
                W = layer.get_weights()

                for idx,_lambda in enumerate(l1l2_lambda_k + l1l2_lambda_b):
                    if _lambda != 0:
                        _pow = 2**(idx % 2) # 1 if idx is even (l1), 2 if odd (l2)
                        l1l2_loss += _lambda*np.sum(np.abs(W[idx//2])**_pow)
    return l1l2_loss

def _l1l2_rnn_loss(layer):
    l1l2_loss = 0
    if 'backward_layer' in layer.__dict__:
        bidirectional = True
        _layer = layer.layer
    else:
        _layer = layer
        bidirectional = False
    ldict = _layer.cell.__dict__

    if 'kernel_regularizer'    in ldict or \
       'recurrent_regularizer' in ldict or \
       'bias_regularizer'      in ldict:
        l1l2_lambda_k, l1l2_lambda_r, l1l2_lambda_b = [0,0], [0,0], [0,0]
        if ldict['kernel_regularizer']    is not None:
            l1l2_lambda_k = list(_layer.kernel_regularizer.__dict__.values())
        if ldict['recurrent_regularizer'] is not None:
            l1l2_lambda_r = list(_layer.recurrent_regularizer.__dict__.values())
        if ldict['bias_regularizer']      is not None:
            l1l2_lambda_b = list(_layer.bias_regularizer.__dict__.values())

        all_lambda = l1l2_lambda_k + l1l2_lambda_r + l1l2_lambda_b
        if any([(_lambda != 0) for _lambda in all_lambda]):
            W = layer.get_weights()
            idx_incr = len(W)//2 # accounts for 'use_bias'

            for idx,_lambda in enumerate(all_lambda):
                if _lambda != 0:
                    _pow = 2**(idx % 2) # 1 if idx is even (l1), 2 if odd (l2)
                    l1l2_loss += _lambda*np.sum(np.abs(W[idx//2])**_pow)
                    if bidirectional:
                        l1l2_loss += _lambda*np.sum(
                                    np.abs(W[idx//2 + idx_incr])**_pow)
        return l1l2_loss  

---

TESTING IMPLEMENTATION:

from keras.layers import Input, Dense, LSTM, GRU, Bidirectional
from keras.models import Model
from keras.regularizers import l1, l2, l1_l2
import numpy as np 

ipt   = Input(shape=(1200,16))
x     = LSTM(60, activation='relu', return_sequences=True,
                                                 recurrent_regularizer=l2(1e-3),)(ipt)
x     = Bidirectional(GRU(60, activation='relu', bias_regularizer     =l1(1e-4)))(x)
out   = Dense(1,  activation='sigmoid',          kernel_regularizer   =l1_l2(2e-4))(x)
model = Model(ipt,out)

model.compile(loss='binary_crossentropy', optimizer='adam')

X = np.random.rand(10,1200,16) # (batch_size, timesteps, input_dim)
Y = np.random.randint(0,2,(10,1))
class_weights = {'0':1, '1': 6}
sample_weights = np.array([class_weights[str(label[0])] for label in Y])

keras_loss   = model.evaluate(X,Y,sample_weight=sample_weights)
custom_loss  = binary_crossentropy(Y, model.predict(X))
custom_loss += l1l2_weight_loss(model)

print('%.6f'%keras_loss  + ' -- keras_loss')
print('%.6f'%custom_loss + ' -- custom_loss') 

0.763822 -- keras_loss
0.763822 -- custom_loss