forked from senior-sigan/ML_Workshop_2017-12-17
/
dnn_log.txt
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/
dnn_log.txt
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➔ py model
Using TensorFlow backend.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 200) 157000
_________________________________________________________________
dropout_1 (Dropout) (None, 200) 0
_________________________________________________________________
dense_2 (Dense) (None, 100) 20100
_________________________________________________________________
dropout_2 (Dropout) (None, 100) 0
_________________________________________________________________
dense_3 (Dense) (None, 60) 6060
_________________________________________________________________
dropout_3 (Dropout) (None, 60) 0
_________________________________________________________________
dense_4 (Dense) (None, 60) 3660
_________________________________________________________________
dense_5 (Dense) (None, 10) 610
=================================================================
Total params: 187,430
Trainable params: 187,430
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/5
59904/60000 [============================>.] - ETA: 0s - loss: 0.9273 - categorical_accuracy: 0.6955Epoch 00001: val_categorical_accuracy improved from -inf to 0.93590, saving model to ./checkpoints/dnn_1513415361/weights-improvement-01-0.94.hdf5
60000/60000 [==============================] - 6s 94us/step - loss: 0.9263 - categorical_accuracy: 0.6958 - val_loss: 0.2329 - val_categorical_accuracy: 0.9359
Epoch 2/5
59648/60000 [============================>.] - ETA: 0s - loss: 0.3927 - categorical_accuracy: 0.8931Epoch 00002: val_categorical_accuracy improved from 0.93590 to 0.95000, saving model to ./checkpoints/dnn_1513415361/weights-improvement-02-0.95.hdf5
60000/60000 [==============================] - 6s 96us/step - loss: 0.3928 - categorical_accuracy: 0.8930 - val_loss: 0.1750 - val_categorical_accuracy: 0.9500
Epoch 3/5
59904/60000 [============================>.] - ETA: 0s - loss: 0.3072 - categorical_accuracy: 0.9192Epoch 00003: val_categorical_accuracy improved from 0.95000 to 0.95950, saving model to ./checkpoints/dnn_1513415361/weights-improvement-03-0.96.hdf5
60000/60000 [==============================] - 5s 90us/step - loss: 0.3071 - categorical_accuracy: 0.9193 - val_loss: 0.1511 - val_categorical_accuracy: 0.9595
Epoch 4/5
59648/60000 [============================>.] - ETA: 0s - loss: 0.2623 - categorical_accuracy: 0.9307Epoch 00004: val_categorical_accuracy improved from 0.95950 to 0.96060, saving model to ./checkpoints/dnn_1513415361/weights-improvement-04-0.96.hdf5
60000/60000 [==============================] - 6s 96us/step - loss: 0.2617 - categorical_accuracy: 0.9309 - val_loss: 0.1512 - val_categorical_accuracy: 0.9606
Epoch 5/5
59392/60000 [============================>.] - ETA: 0s - loss: 0.2405 - categorical_accuracy: 0.9380Epoch 00005: val_categorical_accuracy improved from 0.96060 to 0.96630, saving model to ./checkpoints/dnn_1513415361/weights-improvement-05-0.97.hdf5
60000/60000 [==============================] - 6s 94us/step - loss: 0.2405 - categorical_accuracy: 0.9380 - val_loss: 0.1264 - val_categorical_accuracy: 0.9663
Converted 10 variables to const ops.
97 ops in the final graph.
graph saved!
10000/10000 [==============================] - 1s 148us/step
Test loss: 0.126438725198
Test accuracy: 0.9663