BinaryNet on CIFAR10

In this example we demonstrate how to use Larq to build and train BinaryNet on the CIFAR10 dataset to achieve a validation accuracy approximately 83% on laptop hardware. On a Nvidia GTX 1050 Ti Max-Q it takes approximately 200 minutes to train. For simplicity, compared to the original papers BinaryConnect: Training Deep Neural Networks with binary weights during propagations, and Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, we do not impliment learning rate scaling, or image whitening.

import tensorflow as tf
import larq as lq
import numpy as np
import matplotlib.pyplot as plt

Import CIFAR10 Dataset

We download and normalize the CIFAR10 dataset.

num_classes = 10

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()

train_images = train_images.reshape((50000, 32, 32, 3)).astype("float32")
test_images = test_images.reshape((10000, 32, 32, 3)).astype("float32")

# Normalize pixel values to be between -1 and 1
train_images, test_images = train_images / 127.5 - 1, test_images / 127.5 - 1

train_labels = tf.keras.utils.to_categorical(train_labels, num_classes)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes)

Build BinaryNet

Here we build the BinaryNet model layer by layer using the Keras Sequential API.

# All quantized layers except the first will use the same options
kwargs = dict(input_quantizer="ste_sign",
              kernel_quantizer="ste_sign",
              kernel_constraint="weight_clip",
              use_bias=False)

model = tf.keras.models.Sequential([
    # In the first layer we only quantize the weights and not the input
    lq.layers.QuantConv2D(128, 3,
                          kernel_quantizer="ste_sign",
                          kernel_constraint="weight_clip",
                          use_bias=False,
                          input_shape=(32, 32, 3)),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantConv2D(128, 3, padding="same", **kwargs),
    tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantConv2D(256, 3, padding="same", **kwargs),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantConv2D(256, 3, padding="same", **kwargs),
    tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantConv2D(512, 3, padding="same", **kwargs),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantConv2D(512, 3, padding="same", **kwargs),
    tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
    tf.keras.layers.Flatten(),

    lq.layers.QuantDense(1024, **kwargs),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantDense(1024, **kwargs),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),

    lq.layers.QuantDense(10, **kwargs),
    tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
    tf.keras.layers.Activation("softmax")
])

One can output a summary of the model:

lq.models.summary(model)

+sequential_1 stats-------------------------------------------------------------------+
| Layer                   Input prec.            Outputs   # 1-bit  # 32-bit   Memory |
|                               (bit)                                            (kB) |
+-------------------------------------------------------------------------------------+
| quant_conv2d_6                    -  (-1, 30, 30, 128)      3456         0     0.42 |
| batch_normalization_9             -  (-1, 30, 30, 128)         0       384     1.50 |
| quant_conv2d_7                    1  (-1, 30, 30, 128)    147456         0    18.00 |
| max_pooling2d_3                   -  (-1, 15, 15, 128)         0         0     0.00 |
| batch_normalization_10            -  (-1, 15, 15, 128)         0       384     1.50 |
| quant_conv2d_8                    1  (-1, 15, 15, 256)    294912         0    36.00 |
| batch_normalization_11            -  (-1, 15, 15, 256)         0       768     3.00 |
| quant_conv2d_9                    1  (-1, 15, 15, 256)    589824         0    72.00 |
| max_pooling2d_4                   -    (-1, 7, 7, 256)         0         0     0.00 |
| batch_normalization_12            -    (-1, 7, 7, 256)         0       768     3.00 |
| quant_conv2d_10                   1    (-1, 7, 7, 512)   1179648         0   144.00 |
| batch_normalization_13            -    (-1, 7, 7, 512)         0      1536     6.00 |
| quant_conv2d_11                   1    (-1, 7, 7, 512)   2359296         0   288.00 |
| max_pooling2d_5                   -    (-1, 3, 3, 512)         0         0     0.00 |
| batch_normalization_14            -    (-1, 3, 3, 512)         0      1536     6.00 |
| flatten_1                         -         (-1, 4608)         0         0     0.00 |
| quant_dense_3                     1         (-1, 1024)   4718592         0   576.00 |
| batch_normalization_15            -         (-1, 1024)         0      3072    12.00 |
| quant_dense_4                     1         (-1, 1024)   1048576         0   128.00 |
| batch_normalization_16            -         (-1, 1024)         0      3072    12.00 |
| quant_dense_5                     1           (-1, 10)     10240         0     1.25 |
| batch_normalization_17            -           (-1, 10)         0        30     0.12 |
| activation_1                      -           (-1, 10)         0         0     0.00 |
+-------------------------------------------------------------------------------------+
| Total                                                   10352000     11550  1308.79 |
+-------------------------------------------------------------------------------------+
+sequential_1 summary-------------+
| Total params           10363550 |
| Trainable params       10355850 |
| Non-trainable params   7700     |
| Float-32 Equivalent    39.53 MB |
| Compression of Memory  30.93    |
+---------------------------------+

Model Training

Compile the model and train the model

model.compile(
    tf.keras.optimizers.Adam(lr=0.01,decay=0.0001),
    loss="categorical_crossentropy",
    metrics=["accuracy"],
)

trained_model = model.fit(
    train_images, 
    train_labels,
    batch_size=50, 
    epochs=100,
    validation_data=(test_images, test_labels),
    shuffle=True
)

Train on 50000 samples, validate on 10000 samples
Epoch 1/100
50000/50000 [==============================] - 131s 3ms/step - loss: 1.5733 - acc: 0.4533 - val_loss: 1.6368 - val_acc: 0.4244
Epoch 2/100
50000/50000 [==============================] - 125s 3ms/step - loss: 1.1485 - acc: 0.6387 - val_loss: 1.8497 - val_acc: 0.3764
Epoch 3/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.9641 - acc: 0.7207 - val_loss: 1.5696 - val_acc: 0.4794
Epoch 4/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.8452 - acc: 0.7728 - val_loss: 1.5765 - val_acc: 0.4669
Epoch 5/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.7553 - acc: 0.8114 - val_loss: 1.0653 - val_acc: 0.6928
Epoch 6/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.6841 - acc: 0.8447 - val_loss: 1.0944 - val_acc: 0.6880
Epoch 7/100
50000/50000 [==============================] - 125s 3ms/step - loss: 0.6356 - acc: 0.8685 - val_loss: 0.9909 - val_acc: 0.7317
Epoch 8/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.5907 - acc: 0.8910 - val_loss: 0.9453 - val_acc: 0.7446
Epoch 9/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.5610 - acc: 0.9043 - val_loss: 0.9441 - val_acc: 0.7460
Epoch 10/100
50000/50000 [==============================] - 125s 3ms/step - loss: 0.5295 - acc: 0.9201 - val_loss: 0.8892 - val_acc: 0.7679
Epoch 11/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.5100 - acc: 0.9309 - val_loss: 0.8808 - val_acc: 0.7818
Epoch 12/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4926 - acc: 0.9397 - val_loss: 0.8404 - val_acc: 0.7894
Epoch 13/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.4807 - acc: 0.9470 - val_loss: 0.8600 - val_acc: 0.7928
Epoch 14/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4661 - acc: 0.9529 - val_loss: 0.9046 - val_acc: 0.7732
Epoch 15/100
50000/50000 [==============================] - 125s 3ms/step - loss: 0.4588 - acc: 0.9571 - val_loss: 0.8505 - val_acc: 0.7965
Epoch 16/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4558 - acc: 0.9593 - val_loss: 0.8748 - val_acc: 0.7859
Epoch 17/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4434 - acc: 0.9649 - val_loss: 0.9109 - val_acc: 0.7656
Epoch 18/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.4449 - acc: 0.9643 - val_loss: 0.8532 - val_acc: 0.7971
Epoch 19/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4349 - acc: 0.9701 - val_loss: 0.8677 - val_acc: 0.7951
Epoch 20/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.4351 - acc: 0.9698 - val_loss: 0.9145 - val_acc: 0.7740
Epoch 21/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4268 - acc: 0.9740 - val_loss: 0.8308 - val_acc: 0.8065
Epoch 22/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4243 - acc: 0.9741 - val_loss: 0.8229 - val_acc: 0.8075
Epoch 23/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4201 - acc: 0.9764 - val_loss: 0.8411 - val_acc: 0.8062
Epoch 24/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.4190 - acc: 0.9769 - val_loss: 0.8649 - val_acc: 0.7951
Epoch 25/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4139 - acc: 0.9787 - val_loss: 0.8257 - val_acc: 0.8071
Epoch 26/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4154 - acc: 0.9779 - val_loss: 0.8041 - val_acc: 0.8205
Epoch 27/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4128 - acc: 0.9798 - val_loss: 0.8296 - val_acc: 0.8115
Epoch 28/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.4121 - acc: 0.9798 - val_loss: 0.8241 - val_acc: 0.8074
Epoch 29/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.4093 - acc: 0.9807 - val_loss: 0.8575 - val_acc: 0.7913
Epoch 30/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.4048 - acc: 0.9826 - val_loss: 0.8118 - val_acc: 0.8166
Epoch 31/100
50000/50000 [==============================] - 126s 3ms/step - loss: 0.4041 - acc: 0.9837 - val_loss: 0.8375 - val_acc: 0.8082
Epoch 32/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.4045 - acc: 0.9831 - val_loss: 0.8604 - val_acc: 0.8091
Epoch 33/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4047 - acc: 0.9823 - val_loss: 0.8797 - val_acc: 0.7931
Epoch 34/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.4023 - acc: 0.9842 - val_loss: 0.8694 - val_acc: 0.8020
Epoch 35/100
50000/50000 [==============================] - 125s 3ms/step - loss: 0.3995 - acc: 0.9858 - val_loss: 0.8161 - val_acc: 0.8186
Epoch 36/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3976 - acc: 0.9859 - val_loss: 0.8495 - val_acc: 0.7988
Epoch 37/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.4021 - acc: 0.9847 - val_loss: 0.8542 - val_acc: 0.8062
Epoch 38/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.3939 - acc: 0.9869 - val_loss: 0.8347 - val_acc: 0.8122
Epoch 39/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.3955 - acc: 0.9856 - val_loss: 0.8521 - val_acc: 0.7993
Epoch 40/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3907 - acc: 0.9885 - val_loss: 0.9023 - val_acc: 0.7992
Epoch 41/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3911 - acc: 0.9873 - val_loss: 0.8597 - val_acc: 0.8010
Epoch 42/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3917 - acc: 0.9885 - val_loss: 0.8968 - val_acc: 0.7936
Epoch 43/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3931 - acc: 0.9874 - val_loss: 0.8318 - val_acc: 0.8169
Epoch 44/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3897 - acc: 0.9893 - val_loss: 0.8811 - val_acc: 0.7988
Epoch 45/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3876 - acc: 0.9888 - val_loss: 0.8453 - val_acc: 0.8094
Epoch 46/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3876 - acc: 0.9889 - val_loss: 0.8195 - val_acc: 0.8179
Epoch 47/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3891 - acc: 0.9890 - val_loss: 0.8373 - val_acc: 0.8137
Epoch 48/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3902 - acc: 0.9888 - val_loss: 0.8457 - val_acc: 0.8120
Epoch 49/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3864 - acc: 0.9903 - val_loss: 0.9012 - val_acc: 0.7907
Epoch 50/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3859 - acc: 0.9903 - val_loss: 0.8291 - val_acc: 0.8053
Epoch 51/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3830 - acc: 0.9915 - val_loss: 0.8494 - val_acc: 0.8139
Epoch 52/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3828 - acc: 0.9907 - val_loss: 0.8447 - val_acc: 0.8135
Epoch 53/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3823 - acc: 0.9910 - val_loss: 0.8539 - val_acc: 0.8120
Epoch 54/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3832 - acc: 0.9905 - val_loss: 0.8592 - val_acc: 0.8098
Epoch 55/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3823 - acc: 0.9908 - val_loss: 0.8585 - val_acc: 0.8087
Epoch 56/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3817 - acc: 0.9911 - val_loss: 0.8840 - val_acc: 0.7889
Epoch 57/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3827 - acc: 0.9914 - val_loss: 0.8205 - val_acc: 0.8250
Epoch 58/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3818 - acc: 0.9912 - val_loss: 0.8571 - val_acc: 0.8051
Epoch 59/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3811 - acc: 0.9919 - val_loss: 0.8155 - val_acc: 0.8254
Epoch 60/100
50000/50000 [==============================] - 125s 3ms/step - loss: 0.3803 - acc: 0.9919 - val_loss: 0.8617 - val_acc: 0.8040
Epoch 61/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.3793 - acc: 0.9926 - val_loss: 0.8212 - val_acc: 0.8192
Epoch 62/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3825 - acc: 0.9912 - val_loss: 0.8139 - val_acc: 0.8277
Epoch 63/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.3784 - acc: 0.9923 - val_loss: 0.8304 - val_acc: 0.8121
Epoch 64/100
50000/50000 [==============================] - 125s 2ms/step - loss: 0.3809 - acc: 0.9918 - val_loss: 0.7961 - val_acc: 0.8289
Epoch 65/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3750 - acc: 0.9930 - val_loss: 0.8676 - val_acc: 0.8110
Epoch 66/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3789 - acc: 0.9928 - val_loss: 0.8308 - val_acc: 0.8148
Epoch 67/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3783 - acc: 0.9929 - val_loss: 0.8595 - val_acc: 0.8097
Epoch 68/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3758 - acc: 0.9935 - val_loss: 0.8359 - val_acc: 0.8065
Epoch 69/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3784 - acc: 0.9927 - val_loss: 0.8189 - val_acc: 0.8255
Epoch 70/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3786 - acc: 0.9924 - val_loss: 0.8754 - val_acc: 0.8001
Epoch 71/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3749 - acc: 0.9936 - val_loss: 0.8188 - val_acc: 0.8262
Epoch 72/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3758 - acc: 0.9932 - val_loss: 0.8540 - val_acc: 0.8169
Epoch 73/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3740 - acc: 0.9934 - val_loss: 0.8127 - val_acc: 0.8258
Epoch 74/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3749 - acc: 0.9932 - val_loss: 0.8662 - val_acc: 0.8018
Epoch 75/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3721 - acc: 0.9941 - val_loss: 0.8359 - val_acc: 0.8213
Epoch 76/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3746 - acc: 0.9937 - val_loss: 0.8462 - val_acc: 0.8178
Epoch 77/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3741 - acc: 0.9936 - val_loss: 0.8983 - val_acc: 0.7972
Epoch 78/100
50000/50000 [==============================] - 122s 2ms/step - loss: 0.3751 - acc: 0.9933 - val_loss: 0.8525 - val_acc: 0.8173
Epoch 79/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3762 - acc: 0.9931 - val_loss: 0.8190 - val_acc: 0.8201
Epoch 80/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3737 - acc: 0.9940 - val_loss: 0.8441 - val_acc: 0.8196
Epoch 81/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3729 - acc: 0.9935 - val_loss: 0.8151 - val_acc: 0.8267
Epoch 82/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3735 - acc: 0.9938 - val_loss: 0.8405 - val_acc: 0.8163
Epoch 83/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3723 - acc: 0.9939 - val_loss: 0.8225 - val_acc: 0.8243
Epoch 84/100
50000/50000 [==============================] - 123s 2ms/step - loss: 0.3738 - acc: 0.9938 - val_loss: 0.8413 - val_acc: 0.8115
Epoch 85/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3714 - acc: 0.9947 - val_loss: 0.9080 - val_acc: 0.7932
Epoch 86/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3744 - acc: 0.9942 - val_loss: 0.8467 - val_acc: 0.8135
Epoch 87/100
50000/50000 [==============================] - 124s 2ms/step - loss: 0.3705 - acc: 0.9948 - val_loss: 0.8491 - val_acc: 0.8163
Epoch 88/100
50000/50000 [==============================] - 128s 3ms/step - loss: 0.3733 - acc: 0.9944 - val_loss: 0.8005 - val_acc: 0.8214
Epoch 89/100
50000/50000 [==============================] - 134s 3ms/step - loss: 0.3693 - acc: 0.9949 - val_loss: 0.7791 - val_acc: 0.8321
Epoch 90/100
50000/50000 [==============================] - 135s 3ms/step - loss: 0.3724 - acc: 0.9942 - val_loss: 0.8458 - val_acc: 0.8124
Epoch 91/100
50000/50000 [==============================] - 128s 3ms/step - loss: 0.3732 - acc: 0.9947 - val_loss: 0.8315 - val_acc: 0.8164
Epoch 92/100
50000/50000 [==============================] - 127s 3ms/step - loss: 0.3699 - acc: 0.9950 - val_loss: 0.8140 - val_acc: 0.8226
Epoch 93/100
50000/50000 [==============================] - 131s 3ms/step - loss: 0.3694 - acc: 0.9950 - val_loss: 0.8342 - val_acc: 0.8210
Epoch 94/100
50000/50000 [==============================] - 134s 3ms/step - loss: 0.3698 - acc: 0.9946 - val_loss: 0.8938 - val_acc: 0.8019
Epoch 95/100
50000/50000 [==============================] - 133s 3ms/step - loss: 0.3698 - acc: 0.9946 - val_loss: 0.8771 - val_acc: 0.8066
Epoch 96/100
50000/50000 [==============================] - 164s 3ms/step - loss: 0.3712 - acc: 0.9946 - val_loss: 0.8396 - val_acc: 0.8211
Epoch 97/100
50000/50000 [==============================] - 155s 3ms/step - loss: 0.3689 - acc: 0.9949 - val_loss: 0.8728 - val_acc: 0.8112
Epoch 98/100
50000/50000 [==============================] - 133s 3ms/step - loss: 0.3663 - acc: 0.9953 - val_loss: 0.9615 - val_acc: 0.7902
Epoch 99/100
50000/50000 [==============================] - 133s 3ms/step - loss: 0.3714 - acc: 0.9944 - val_loss: 0.8414 - val_acc: 0.8188
Epoch 100/100
50000/50000 [==============================] - 138s 3ms/step - loss: 0.3682 - acc: 0.9956 - val_loss: 0.8055 - val_acc: 0.8266

Model Output

We can now plot the final validation accuracy and loss:

plt.plot(trained_model.history['acc'])
plt.plot(trained_model.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')

print(np.max(trained_model.history['acc']))
print(np.max(trained_model.history['val_acc']))

0.9956000019311905
0.8320999944210052

png

plt.plot(trained_model.history['loss'])
plt.plot(trained_model.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')

print(np.min(trained_model.history['loss']))
print(np.min(trained_model.history['val_loss']))

0.3663262344896793
0.7790719392895699

png