(tensorflow_macos_venv) deeplearner@Macbook dl_pro % python simple_mnist_convent.py
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0         
_________________________________________________________________
dropout (Dropout)            (None, 1600)              0         
_________________________________________________________________
dense (Dense)                (None, 10)                16010     
=================================================================
Total params: 34,826
Trainable params: 34,826
Non-trainable params: 0
_________________________________________________________________
2021-06-20 11:45:58.674654: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-06-20 11:45:58.674934: W tensorflow/core/platform/profile_utils/cpu_utils.cc:126] Failed to get CPU frequency: 0 Hz
Epoch 1/15
422/422 [==============================] - 7s 17ms/step - loss: 0.8916 - accuracy: 0.7361 - val_loss: 2.5361 - val_accuracy: 0.8635
Epoch 2/15
422/422 [==============================] - 7s 17ms/step - loss: 0.2899 - accuracy: 0.9364 - val_loss: 3.4547 - val_accuracy: 0.8693
Epoch 3/15
422/422 [==============================] - 7s 16ms/step - loss: 0.2920 - accuracy: 0.9443 - val_loss: 4.7374 - val_accuracy: 0.8643
Epoch 4/15
422/422 [==============================] - 7s 17ms/step - loss: 0.3603 - accuracy: 0.9486 - val_loss: 5.6765 - val_accuracy: 0.8698
Epoch 5/15
422/422 [==============================] - 7s 17ms/step - loss: 0.3801 - accuracy: 0.9547 - val_loss: 6.9571 - val_accuracy: 0.8728
Epoch 6/15
422/422 [==============================] - 7s 17ms/step - loss: 0.4166 - accuracy: 0.9584 - val_loss: 9.4058 - val_accuracy: 0.8742
Epoch 7/15
422/422 [==============================] - 7s 16ms/step - loss: 0.4847 - accuracy: 0.9592 - val_loss: 11.2112 - val_accuracy: 0.8732
Epoch 8/15
422/422 [==============================] - 7s 16ms/step - loss: 0.5894 - accuracy: 0.9590 - val_loss: 13.8399 - val_accuracy: 0.8765
Epoch 9/15
422/422 [==============================] - 7s 17ms/step - loss: 0.6886 - accuracy: 0.9603 - val_loss: 16.2775 - val_accuracy: 0.8743
Epoch 10/15
422/422 [==============================] - 7s 17ms/step - loss: 0.7712 - accuracy: 0.9608 - val_loss: 18.9946 - val_accuracy: 0.8728
Epoch 11/15
422/422 [==============================] - 7s 17ms/step - loss: 0.8119 - accuracy: 0.9639 - val_loss: 25.3893 - val_accuracy: 0.8723
Epoch 12/15
422/422 [==============================] - 7s 16ms/step - loss: 0.9099 - accuracy: 0.9646 - val_loss: 25.7771 - val_accuracy: 0.8733
Epoch 13/15
422/422 [==============================] - 7s 16ms/step - loss: 1.0090 - accuracy: 0.9639 - val_loss: 32.2646 - val_accuracy: 0.8732
Epoch 14/15
422/422 [==============================] - 7s 17ms/step - loss: 1.2163 - accuracy: 0.9650 - val_loss: 29.7056 - val_accuracy: 0.8795
Epoch 15/15
422/422 [==============================] - 7s 17ms/step - loss: 1.1836 - accuracy: 0.9682 - val_loss: 36.4057 - val_accuracy: 0.8768
Test loss: 0.5519317388534546
Test accuracy: 0.9843999743461609
(tensorflow_macos_venv) deeplearner@Macbook dl_pro %