Same code run on two different machines, disparity in accuracy; From Deep Learning with Python Chapter 5.3 pretrained convnet

Question

I'm following along with Chollet's book Deep Learning with Python and in chapter 5.3 I've come across a weird accuracy disparity between myself and the author.

After running the exact code pulled from the github, obtainable here I'm getting

test acc: 0.9409999930858612

while the author is getting

test acc: 0.967999992371

Also, when initially starting to train the models I am usually 10% behind versus when the author starts. Here are all of my outputs in the order in which they appear on that github link.

I'm looking for any pointers as to why running the same code is leaving such a huge gap. Thanks for taking a look!

First

Train on 2000 samples, validate on 1000 samples
Epoch 1/30
2000/2000 [==============================] - 1s 392us/step - loss: 0.6145 - acc: 0.6570 - val_loss: 0.4502 - val_acc: 0.8250
Epoch 2/30
2000/2000 [==============================] - 1s 260us/step - loss: 0.4402 - acc: 0.7980 - val_loss: 0.3596 - val_acc: 0.8600
Epoch 3/30
2000/2000 [==============================] - 1s 258us/step - loss: 0.3559 - acc: 0.8420 - val_loss: 0.3238 - val_acc: 0.8710
Epoch 4/30
2000/2000 [==============================] - 1s 257us/step - loss: 0.3149 - acc: 0.8655 - val_loss: 0.2945 - val_acc: 0.8800
Epoch 5/30
2000/2000 [==============================] - 1s 259us/step - loss: 0.2895 - acc: 0.8850 - val_loss: 0.2905 - val_acc: 0.8710
Epoch 6/30
2000/2000 [==============================] - 1s 257us/step - loss: 0.2627 - acc: 0.8970 - val_loss: 0.2695 - val_acc: 0.8950
Epoch 7/30
2000/2000 [==============================] - 1s 265us/step - loss: 0.2450 - acc: 0.9040 - val_loss: 0.2608 - val_acc: 0.8930
Epoch 8/30
2000/2000 [==============================] - 1s 259us/step - loss: 0.2328 - acc: 0.9150 - val_loss: 0.2937 - val_acc: 0.8670
Epoch 9/30
2000/2000 [==============================] - 1s 260us/step - loss: 0.2208 - acc: 0.9170 - val_loss: 0.2933 - val_acc: 0.8660
Epoch 10/30
2000/2000 [==============================] - 1s 254us/step - loss: 0.2026 - acc: 0.9225 - val_loss: 0.2471 - val_acc: 0.9040
Epoch 11/30
2000/2000 [==============================] - 1s 259us/step - loss: 0.1954 - acc: 0.9260 - val_loss: 0.2461 - val_acc: 0.9000
Epoch 12/30
2000/2000 [==============================] - 1s 260us/step - loss: 0.1786 - acc: 0.9360 - val_loss: 0.2414 - val_acc: 0.9070
Epoch 13/30
2000/2000 [==============================] - 0s 248us/step - loss: 0.1781 - acc: 0.9305 - val_loss: 0.2410 - val_acc: 0.9080
Epoch 14/30
2000/2000 [==============================] - 0s 249us/step - loss: 0.1701 - acc: 0.9380 - val_loss: 0.2372 - val_acc: 0.9080
Epoch 15/30
2000/2000 [==============================] - 1s 257us/step - loss: 0.1624 - acc: 0.9450 - val_loss: 0.2403 - val_acc: 0.9050
Epoch 16/30
2000/2000 [==============================] - 1s 258us/step - loss: 0.1580 - acc: 0.9465 - val_loss: 0.2448 - val_acc: 0.9060
Epoch 17/30
2000/2000 [==============================] - 1s 256us/step - loss: 0.1467 - acc: 0.9520 - val_loss: 0.2347 - val_acc: 0.9050
Epoch 18/30
2000/2000 [==============================] - 1s 255us/step - loss: 0.1421 - acc: 0.9505 - val_loss: 0.2366 - val_acc: 0.9020
Epoch 19/30
2000/2000 [==============================] - 1s 258us/step - loss: 0.1375 - acc: 0.9540 - val_loss: 0.2327 - val_acc: 0.9080
Epoch 20/30
2000/2000 [==============================] - 0s 248us/step - loss: 0.1268 - acc: 0.9545 - val_loss: 0.2395 - val_acc: 0.9030
Epoch 21/30
2000/2000 [==============================] - 1s 255us/step - loss: 0.1216 - acc: 0.9565 - val_loss: 0.2436 - val_acc: 0.9040
Epoch 22/30
2000/2000 [==============================] - 1s 255us/step - loss: 0.1220 - acc: 0.9565 - val_loss: 0.2340 - val_acc: 0.9040
Epoch 23/30
2000/2000 [==============================] - 1s 261us/step - loss: 0.1152 - acc: 0.9630 - val_loss: 0.2328 - val_acc: 0.9030
Epoch 24/30
2000/2000 [==============================] - 1s 251us/step - loss: 0.1111 - acc: 0.9605 - val_loss: 0.2506 - val_acc: 0.8990
Epoch 25/30
2000/2000 [==============================] - 1s 257us/step - loss: 0.1024 - acc: 0.9665 - val_loss: 0.2391 - val_acc: 0.9040
Epoch 26/30
2000/2000 [==============================] - 0s 250us/step - loss: 0.0999 - acc: 0.9680 - val_loss: 0.2573 - val_acc: 0.8980
Epoch 27/30
2000/2000 [==============================] - 1s 261us/step - loss: 0.0996 - acc: 0.9680 - val_loss: 0.2365 - val_acc: 0.9060
Epoch 28/30
2000/2000 [==============================] - 0s 250us/step - loss: 0.0873 - acc: 0.9765 - val_loss: 0.2444 - val_acc: 0.9020
Epoch 29/30
2000/2000 [==============================] - 0s 244us/step - loss: 0.0904 - acc: 0.9730 - val_loss: 0.2494 - val_acc: 0.9020
Epoch 30/30
2000/2000 [==============================] - 0s 245us/step - loss: 0.0876 - acc: 0.9745 - val_loss: 0.2426 - val_acc: 0.9020

Second

Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/30
 - 13s - loss: 0.6106 - acc: 0.6725 - val_loss: 0.4488 - val_acc: 0.8300
Epoch 2/30
 - 12s - loss: 0.4856 - acc: 0.7820 - val_loss: 0.3938 - val_acc: 0.8290
Epoch 3/30
 - 12s - loss: 0.4271 - acc: 0.8125 - val_loss: 0.3307 - val_acc: 0.8690
Epoch 4/30
 - 12s - loss: 0.4046 - acc: 0.8215 - val_loss: 0.3040 - val_acc: 0.8780
Epoch 5/30
 - 12s - loss: 0.3809 - acc: 0.8275 - val_loss: 0.2999 - val_acc: 0.8670
Epoch 6/30
 - 12s - loss: 0.3592 - acc: 0.8510 - val_loss: 0.2794 - val_acc: 0.8890
Epoch 7/30
 - 12s - loss: 0.3709 - acc: 0.8350 - val_loss: 0.2703 - val_acc: 0.8950
Epoch 8/30
 - 12s - loss: 0.3460 - acc: 0.8525 - val_loss: 0.2683 - val_acc: 0.8940
Epoch 9/30
 - 12s - loss: 0.3532 - acc: 0.8430 - val_loss: 0.2660 - val_acc: 0.8820
Epoch 10/30
 - 12s - loss: 0.3277 - acc: 0.8545 - val_loss: 0.2641 - val_acc: 0.8950
Epoch 11/30
 - 12s - loss: 0.3236 - acc: 0.8685 - val_loss: 0.2705 - val_acc: 0.8770
Epoch 12/30
 - 12s - loss: 0.3123 - acc: 0.8740 - val_loss: 0.2533 - val_acc: 0.8960
Epoch 13/30
 - 12s - loss: 0.3279 - acc: 0.8605 - val_loss: 0.2718 - val_acc: 0.8740
Epoch 14/30
 - 12s - loss: 0.3088 - acc: 0.8595 - val_loss: 0.2510 - val_acc: 0.9000
Epoch 15/30
 - 12s - loss: 0.2999 - acc: 0.8700 - val_loss: 0.2468 - val_acc: 0.9010
Epoch 16/30
 - 12s - loss: 0.3128 - acc: 0.8600 - val_loss: 0.2496 - val_acc: 0.9020
Epoch 17/30
 - 12s - loss: 0.3064 - acc: 0.8605 - val_loss: 0.2496 - val_acc: 0.9010
Epoch 18/30
 - 12s - loss: 0.3090 - acc: 0.8660 - val_loss: 0.2467 - val_acc: 0.8980
Epoch 19/30
 - 12s - loss: 0.2903 - acc: 0.8710 - val_loss: 0.2709 - val_acc: 0.8790
Epoch 20/30
 - 12s - loss: 0.3012 - acc: 0.8700 - val_loss: 0.2499 - val_acc: 0.8940
Epoch 21/30
 - 12s - loss: 0.2944 - acc: 0.8820 - val_loss: 0.2593 - val_acc: 0.8960
Epoch 22/30
 - 12s - loss: 0.2978 - acc: 0.8670 - val_loss: 0.2421 - val_acc: 0.9040
Epoch 23/30
 - 12s - loss: 0.2942 - acc: 0.8695 - val_loss: 0.2378 - val_acc: 0.9050
Epoch 24/30
 - 12s - loss: 0.2809 - acc: 0.8830 - val_loss: 0.2447 - val_acc: 0.8920
Epoch 25/30
 - 12s - loss: 0.2963 - acc: 0.8765 - val_loss: 0.2420 - val_acc: 0.8950
Epoch 26/30
 - 12s - loss: 0.2869 - acc: 0.8725 - val_loss: 0.2620 - val_acc: 0.8910
Epoch 27/30
 - 12s - loss: 0.2789 - acc: 0.8820 - val_loss: 0.2447 - val_acc: 0.8950
Epoch 28/30
 - 12s - loss: 0.2852 - acc: 0.8745 - val_loss: 0.2488 - val_acc: 0.8990
Epoch 29/30
 - 12s - loss: 0.2821 - acc: 0.8810 - val_loss: 0.2402 - val_acc: 0.9010
Epoch 30/30
 - 12s - loss: 0.2810 - acc: 0.8815 - val_loss: 0.2392 - val_acc: 0.9040

Third

Epoch 1/100
100/100 [==============================] - 13s 130ms/step - loss: 0.2866 - acc: 0.8735 - val_loss: 0.2175 - val_acc: 0.9080
Epoch 2/100
100/100 [==============================] - 12s 119ms/step - loss: 0.2588 - acc: 0.8925 - val_loss: 0.2073 - val_acc: 0.9200
Epoch 3/100
100/100 [==============================] - 12s 121ms/step - loss: 0.2464 - acc: 0.8985 - val_loss: 0.2072 - val_acc: 0.9200
Epoch 4/100
100/100 [==============================] - 12s 121ms/step - loss: 0.2127 - acc: 0.9085 - val_loss: 0.2032 - val_acc: 0.9230
Epoch 5/100
100/100 [==============================] - 12s 120ms/step - loss: 0.2147 - acc: 0.9100 - val_loss: 0.1972 - val_acc: 0.9200
Epoch 6/100
100/100 [==============================] - 12s 118ms/step - loss: 0.1998 - acc: 0.9130 - val_loss: 0.1975 - val_acc: 0.9240
Epoch 7/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1977 - acc: 0.9235 - val_loss: 0.2052 - val_acc: 0.9170
Epoch 8/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1748 - acc: 0.9270 - val_loss: 0.1890 - val_acc: 0.9270
Epoch 9/100
100/100 [==============================] - 12s 119ms/step - loss: 0.1724 - acc: 0.9325 - val_loss: 0.2060 - val_acc: 0.9230
Epoch 10/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1412 - acc: 0.9435 - val_loss: 0.1968 - val_acc: 0.9190
Epoch 11/100
100/100 [==============================] - 12s 119ms/step - loss: 0.1455 - acc: 0.9450 - val_loss: 0.1805 - val_acc: 0.9350
Epoch 12/100
100/100 [==============================] - 12s 119ms/step - loss: 0.1462 - acc: 0.9450 - val_loss: 0.1814 - val_acc: 0.9340
Epoch 13/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1243 - acc: 0.9535 - val_loss: 0.2028 - val_acc: 0.9250
Epoch 14/100
100/100 [==============================] - 12s 119ms/step - loss: 0.1306 - acc: 0.9500 - val_loss: 0.1753 - val_acc: 0.9310
Epoch 15/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1222 - acc: 0.9525 - val_loss: 0.1981 - val_acc: 0.9310
Epoch 16/100
100/100 [==============================] - 12s 119ms/step - loss: 0.1221 - acc: 0.9500 - val_loss: 0.2299 - val_acc: 0.9160
Epoch 17/100
100/100 [==============================] - 12s 120ms/step - loss: 0.1019 - acc: 0.9625 - val_loss: 0.2630 - val_acc: 0.9160
Epoch 18/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0970 - acc: 0.9630 - val_loss: 0.1876 - val_acc: 0.9250
Epoch 19/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0961 - acc: 0.9620 - val_loss: 0.2018 - val_acc: 0.9300
Epoch 20/100
100/100 [==============================] - 12s 121ms/step - loss: 0.1085 - acc: 0.9570 - val_loss: 0.1957 - val_acc: 0.9320
Epoch 21/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0937 - acc: 0.9630 - val_loss: 0.1920 - val_acc: 0.9290
Epoch 22/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0953 - acc: 0.9605 - val_loss: 0.2289 - val_acc: 0.9260
Epoch 23/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0808 - acc: 0.9700 - val_loss: 0.2148 - val_acc: 0.9260
Epoch 24/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0927 - acc: 0.9645 - val_loss: 0.2542 - val_acc: 0.9230
Epoch 25/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0924 - acc: 0.9580 - val_loss: 0.2366 - val_acc: 0.9250
Epoch 26/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0686 - acc: 0.9760 - val_loss: 0.2021 - val_acc: 0.9370
Epoch 27/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0761 - acc: 0.9735 - val_loss: 0.2552 - val_acc: 0.9190
Epoch 28/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0713 - acc: 0.9740 - val_loss: 0.1946 - val_acc: 0.9330
Epoch 29/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0670 - acc: 0.9735 - val_loss: 0.2767 - val_acc: 0.9140
Epoch 30/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0562 - acc: 0.9780 - val_loss: 0.2539 - val_acc: 0.9300
Epoch 31/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0723 - acc: 0.9750 - val_loss: 0.2265 - val_acc: 0.9270
Epoch 32/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0661 - acc: 0.9755 - val_loss: 0.1973 - val_acc: 0.9340
Epoch 33/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0683 - acc: 0.9740 - val_loss: 0.1937 - val_acc: 0.9330
Epoch 34/100
100/100 [==============================] - 12s 121ms/step - loss: 0.0575 - acc: 0.9800 - val_loss: 0.2816 - val_acc: 0.9250
Epoch 35/100
100/100 [==============================] - 12s 123ms/step - loss: 0.0602 - acc: 0.9795 - val_loss: 0.2012 - val_acc: 0.9300
Epoch 36/100
100/100 [==============================] - 12s 122ms/step - loss: 0.0550 - acc: 0.9790 - val_loss: 0.2138 - val_acc: 0.9360
Epoch 37/100
100/100 [==============================] - 12s 124ms/step - loss: 0.0546 - acc: 0.9750 - val_loss: 0.2061 - val_acc: 0.9400
Epoch 38/100
100/100 [==============================] - 12s 121ms/step - loss: 0.0638 - acc: 0.9780 - val_loss: 0.2375 - val_acc: 0.9290
Epoch 39/100
100/100 [==============================] - 12s 122ms/step - loss: 0.0520 - acc: 0.9785 - val_loss: 0.2437 - val_acc: 0.9260
Epoch 40/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0522 - acc: 0.9775 - val_loss: 0.1932 - val_acc: 0.9430
Epoch 41/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0512 - acc: 0.9800 - val_loss: 0.2903 - val_acc: 0.9200
Epoch 42/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0546 - acc: 0.9790 - val_loss: 0.2127 - val_acc: 0.9410
Epoch 43/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0558 - acc: 0.9805 - val_loss: 0.2027 - val_acc: 0.9410
Epoch 44/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0408 - acc: 0.9875 - val_loss: 0.2138 - val_acc: 0.9380
Epoch 45/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0451 - acc: 0.9810 - val_loss: 0.2076 - val_acc: 0.9390
Epoch 46/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0529 - acc: 0.9820 - val_loss: 0.2035 - val_acc: 0.9420
Epoch 47/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0375 - acc: 0.9850 - val_loss: 0.1965 - val_acc: 0.9430
Epoch 48/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0407 - acc: 0.9870 - val_loss: 0.2131 - val_acc: 0.9410
Epoch 49/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0387 - acc: 0.9840 - val_loss: 0.2467 - val_acc: 0.9350
Epoch 50/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0412 - acc: 0.9860 - val_loss: 0.1852 - val_acc: 0.9430
Epoch 51/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0350 - acc: 0.9855 - val_loss: 0.3657 - val_acc: 0.9200
Epoch 52/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0337 - acc: 0.9850 - val_loss: 0.2103 - val_acc: 0.9450
Epoch 53/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0478 - acc: 0.9815 - val_loss: 0.2192 - val_acc: 0.9440
Epoch 54/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0446 - acc: 0.9820 - val_loss: 0.2293 - val_acc: 0.9360
Epoch 55/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0318 - acc: 0.9885 - val_loss: 0.2361 - val_acc: 0.9390
Epoch 56/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0317 - acc: 0.9865 - val_loss: 0.2123 - val_acc: 0.9450
Epoch 57/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0337 - acc: 0.9905 - val_loss: 0.2219 - val_acc: 0.9420
Epoch 58/100
100/100 [==============================] - 12s 120ms/step - loss: 0.0390 - acc: 0.9895 - val_loss: 0.2046 - val_acc: 0.9380
Epoch 59/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0295 - acc: 0.9905 - val_loss: 0.2522 - val_acc: 0.9410
Epoch 60/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0315 - acc: 0.9890 - val_loss: 0.2451 - val_acc: 0.9330

Epoch 61/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0251 - acc: 0.9935 - val_loss: 0.2584 - val_acc: 0.9300
Epoch 62/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0338 - acc: 0.9860 - val_loss: 0.1990 - val_acc: 0.9440
Epoch 63/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0301 - acc: 0.9885 - val_loss: 0.2289 - val_acc: 0.9330
Epoch 64/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0255 - acc: 0.9900 - val_loss: 0.2251 - val_acc: 0.9440
Epoch 65/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0302 - acc: 0.9880 - val_loss: 0.2312 - val_acc: 0.9440
Epoch 66/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0198 - acc: 0.9925 - val_loss: 0.2832 - val_acc: 0.9360
Epoch 67/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0257 - acc: 0.9890 - val_loss: 0.3406 - val_acc: 0.9230
Epoch 68/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0261 - acc: 0.9885 - val_loss: 0.2148 - val_acc: 0.9410
Epoch 69/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0414 - acc: 0.9850 - val_loss: 0.2319 - val_acc: 0.9370
Epoch 70/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0286 - acc: 0.9910 - val_loss: 0.2229 - val_acc: 0.9400
Epoch 71/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0275 - acc: 0.9905 - val_loss: 0.2303 - val_acc: 0.9360
Epoch 72/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0293 - acc: 0.9895 - val_loss: 0.2329 - val_acc: 0.9400
Epoch 73/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0262 - acc: 0.9925 - val_loss: 0.2768 - val_acc: 0.9350
Epoch 74/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0258 - acc: 0.9895 - val_loss: 0.2277 - val_acc: 0.9410
Epoch 75/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0293 - acc: 0.9900 - val_loss: 0.3432 - val_acc: 0.9270
Epoch 76/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0245 - acc: 0.9895 - val_loss: 0.2557 - val_acc: 0.9460
Epoch 77/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0242 - acc: 0.9920 - val_loss: 0.3263 - val_acc: 0.9310
Epoch 78/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0269 - acc: 0.9925 - val_loss: 0.2669 - val_acc: 0.9390
Epoch 79/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0277 - acc: 0.9895 - val_loss: 0.3285 - val_acc: 0.9330
Epoch 80/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0211 - acc: 0.9930 - val_loss: 0.2640 - val_acc: 0.9300
Epoch 81/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0229 - acc: 0.9905 - val_loss: 0.2543 - val_acc: 0.9390
Epoch 82/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0205 - acc: 0.9940 - val_loss: 0.2587 - val_acc: 0.9400
Epoch 83/100
100/100 [==============================] - 12s 117ms/step - loss: 0.0260 - acc: 0.9920 - val_loss: 0.3032 - val_acc: 0.9290
Epoch 84/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0253 - acc: 0.9930 - val_loss: 0.2701 - val_acc: 0.9400
Epoch 85/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0244 - acc: 0.9940 - val_loss: 0.2766 - val_acc: 0.9390
Epoch 86/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0148 - acc: 0.9940 - val_loss: 0.2749 - val_acc: 0.9390
Epoch 87/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0230 - acc: 0.9920 - val_loss: 0.2702 - val_acc: 0.9310
Epoch 88/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0249 - acc: 0.9895 - val_loss: 0.2651 - val_acc: 0.9400
Epoch 89/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0174 - acc: 0.9935 - val_loss: 0.4466 - val_acc: 0.9220
Epoch 90/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0180 - acc: 0.9945 - val_loss: 0.3415 - val_acc: 0.9350
Epoch 91/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0216 - acc: 0.9950 - val_loss: 0.2878 - val_acc: 0.9390
Epoch 92/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0231 - acc: 0.9890 - val_loss: 0.5113 - val_acc: 0.9130
Epoch 93/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0327 - acc: 0.9880 - val_loss: 0.3749 - val_acc: 0.9280
Epoch 94/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0181 - acc: 0.9935 - val_loss: 0.3770 - val_acc: 0.9280
Epoch 95/100
100/100 [==============================] - 12s 117ms/step - loss: 0.0142 - acc: 0.9955 - val_loss: 0.4558 - val_acc: 0.9250
Epoch 96/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0174 - acc: 0.9920 - val_loss: 0.3398 - val_acc: 0.9360
Epoch 97/100
100/100 [==============================] - 12s 119ms/step - loss: 0.0208 - acc: 0.9935 - val_loss: 0.2885 - val_acc: 0.9450
Epoch 98/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0188 - acc: 0.9945 - val_loss: 0.3521 - val_acc: 0.9260
Epoch 99/100
100/100 [==============================] - 12s 117ms/step - loss: 0.0154 - acc: 0.9940 - val_loss: 0.3361 - val_acc: 0.9340
Epoch 100/100
100/100 [==============================] - 12s 118ms/step - loss: 0.0202 - acc: 0.9935 - val_loss: 0.2974 - val_acc: 0.9390

Answer 1

The issue you are pointing out is perfectly normal. In your case, the difference between the starting/final accuracies are negligible so don't worry. If there was a huge difference, ie more than 5-8%, then you should be worried. Overall, there are at least 3 possible explanations:

1. The hardware is different: clearly results in minor accuracy differences.

2. Software differences: Running codes on GPU and CPU will oftentimes result in different but similar results.

3. Weight initialization (WI) might be different. Obviously this does not apply to your situation as you loaded the pertained VGG with the preset weights. Overall, HOW you do WI is a very important thing to consider in training deep nets.