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model.evaluate() gives a different loss on training data from the one in training process #6977

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@alanwang93

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@alanwang93

I'm implementing a CNN model, when I just have few layers, it works well. When I tried a deeper network, I can achieve a high performance (a small loss given during the training process) on training data, but when I use model.evaluate() on training data, I get a poor performance (much greater loss). I wonder why this will happen since the evaluation are all on training data.

Here is what I got:

input_shape = (X.shape[1], X.shape[2], 1)
model = Sequential()

y = [label2id[l] for l in labels.reshape(-1)]
y =  keras.utils.to_categorical(y)

model.add(Conv2D(32, (5, 5), strides=(2,2), input_shape=input_shape))
model.add(Activation('relu'))
model.add(BatchNormalization())


model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Dropout(0.3))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Dropout(0.3))

model.add(Conv2D(512, (1, 1)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Conv2D(15, (1, 1)))
model.add(Activation('relu'))
model.add(BatchNormalization())


model.add(GlobalAveragePooling2D())

model.add(Dense(500, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(15, activation='softmax'))

opt = Adam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

model.fit(np.expand_dims(X, axis=3), y, batch_size=200, epochs=15, validation_data=(np.expand_dims(X_val,3), y_val))

The log during training:

Train on 582 samples, validate on 290 samples
Epoch 1/15
582/582 [==============================] - 14s - loss: 2.6431 - acc: 0.1821 - val_loss: 2.6653 - val_acc: 0.0759
Epoch 2/15
582/582 [==============================] - 12s - loss: 2.3759 - acc: 0.3832 - val_loss: 3.9411 - val_acc: 0.0655
Epoch 3/15
582/582 [==============================] - 13s - loss: 2.0834 - acc: 0.4141 - val_loss: 7.2338 - val_acc: 0.0655
Epoch 4/15
582/582 [==============================] - 13s - loss: 1.8380 - acc: 0.5120 - val_loss: 9.4135 - val_acc: 0.0655
Epoch 5/15
582/582 [==============================] - 13s - loss: 1.6002 - acc: 0.5550 - val_loss: 10.0389 - val_acc: 0.0655
Epoch 6/15
582/582 [==============================] - 13s - loss: 1.3725 - acc: 0.6117 - val_loss: 11.0042 - val_acc: 0.0759
Epoch 7/15
582/582 [==============================] - 13s - loss: 1.1924 - acc: 0.6443 - val_loss: 10.2766 - val_acc: 0.0862
Epoch 8/15
582/582 [==============================] - 13s - loss: 1.0529 - acc: 0.6993 - val_loss: 9.2593 - val_acc: 0.0862
Epoch 9/15
582/582 [==============================] - 13s - loss: 0.9137 - acc: 0.7491 - val_loss: 9.9668 - val_acc: 0.0897
Epoch 10/15
582/582 [==============================] - 13s - loss: 0.7928 - acc: 0.7784 - val_loss: 9.4821 - val_acc: 0.0966
Epoch 11/15
582/582 [==============================] - 13s - loss: 0.6885 - acc: 0.8179 - val_loss: 8.7342 - val_acc: 0.1000
Epoch 12/15
582/582 [==============================] - 12s - loss: 0.6094 - acc: 0.8213 - val_loss: 8.5325 - val_acc: 0.1207
Epoch 13/15
582/582 [==============================] - 12s - loss: 0.5345 - acc: 0.8488 - val_loss: 7.9924 - val_acc: 0.1207
Epoch 14/15
582/582 [==============================] - 12s - loss: 0.4800 - acc: 0.8643 - val_loss: 7.8522 - val_acc: 0.1000
Epoch 15/15
582/582 [==============================] - 12s - loss: 0.4357 - acc: 0.8660 - val_loss: 7.1004 - val_acc: 0.1172

When I evaluate on training data:

score = model.evaluate(np.expand_dims(X, axis=3), y, batch_size=32)
print score
576/582 [============================>.] - ETA: 0s[7.6189327469396426, 0.10309278350515463]

On validation data

score = model.evaluate(np.expand_dims(X_val, axis=3), y_val, batch_size=32)
print score
288/290 [============================>.] - ETA: 0s[7.1004119609964302, 0.11724137931034483]

Could someone help me? Thanks a lot.

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