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# BASIC WALK THROUGH FOR MNIST NN
# BASED ON KERAS TUTORIALS (2019)
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.utils import np_utils
import matplotlib.pyplot as plt
OBTAIN¶
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from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
SCRUB¶
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# FLATTEN 28 x 28 IMAGE TO 784 VECTOR
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
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# NORMALIZE INPUTS FROM RGB COLOR TO 0-1
X_train = X_train / 255
X_test = X_test / 255
# THE OLD ONE HOT ENCODE - CONVERT "CATEGORICAL" CLASSIFICATION TO ENCODE
# A "BINARIZATION" OF THE CATEGORIES
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
MODEL¶
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# BUILD BASELINE
def baseline_model():
# create model
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
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# RUN BASELINE
model = baseline_model()
# FIT THE MODEL
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=300, batch_size=1000, verbose=2)
# EVALUATE THE MODEL
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. Train on 60000 samples, validate on 10000 samples Epoch 1/300 WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead. WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead. - 4s - loss: 0.5222 - acc: 0.8570 - val_loss: 0.2381 - val_acc: 0.9323 Epoch 2/300 - 3s - loss: 0.2010 - acc: 0.9439 - val_loss: 0.1645 - val_acc: 0.9527 Epoch 3/300 - 3s - loss: 0.1454 - acc: 0.9591 - val_loss: 0.1331 - val_acc: 0.9596 Epoch 4/300 - 3s - loss: 0.1122 - acc: 0.9687 - val_loss: 0.1090 - val_acc: 0.9674 Epoch 5/300 - 3s - loss: 0.0916 - acc: 0.9743 - val_loss: 0.0952 - val_acc: 0.9723 Epoch 6/300 - 3s - loss: 0.0740 - acc: 0.9798 - val_loss: 0.0843 - val_acc: 0.9751 Epoch 7/300 - 3s - loss: 0.0617 - acc: 0.9832 - val_loss: 0.0810 - val_acc: 0.9748 Epoch 8/300 - 3s - loss: 0.0525 - acc: 0.9861 - val_loss: 0.0750 - val_acc: 0.9779 Epoch 9/300 - 3s - loss: 0.0438 - acc: 0.9888 - val_loss: 0.0722 - val_acc: 0.9783 Epoch 10/300 - 3s - loss: 0.0372 - acc: 0.9908 - val_loss: 0.0659 - val_acc: 0.9795 Epoch 11/300 - 3s - loss: 0.0317 - acc: 0.9925 - val_loss: 0.0645 - val_acc: 0.9806 Epoch 12/300 - 3s - loss: 0.0277 - acc: 0.9937 - val_loss: 0.0628 - val_acc: 0.9804 Epoch 13/300 - 3s - loss: 0.0244 - acc: 0.9947 - val_loss: 0.0604 - val_acc: 0.9819 Epoch 14/300 - 3s - loss: 0.0206 - acc: 0.9961 - val_loss: 0.0594 - val_acc: 0.9817 Epoch 15/300 - 3s - loss: 0.0179 - acc: 0.9968 - val_loss: 0.0619 - val_acc: 0.9804 Epoch 16/300 - 3s - loss: 0.0153 - acc: 0.9974 - val_loss: 0.0596 - val_acc: 0.9816 Epoch 17/300 - 3s - loss: 0.0132 - acc: 0.9983 - val_loss: 0.0590 - val_acc: 0.9816 Epoch 18/300 - 3s - loss: 0.0116 - acc: 0.9987 - val_loss: 0.0605 - val_acc: 0.9810 Epoch 19/300 - 3s - loss: 0.0102 - acc: 0.9989 - val_loss: 0.0587 - val_acc: 0.9815 Epoch 20/300 - 3s - loss: 0.0088 - acc: 0.9993 - val_loss: 0.0572 - val_acc: 0.9827 Epoch 21/300 - 3s - loss: 0.0078 - acc: 0.9994 - val_loss: 0.0589 - val_acc: 0.9825 Epoch 22/300 - 3s - loss: 0.0068 - acc: 0.9995 - val_loss: 0.0581 - val_acc: 0.9828 Epoch 23/300 - 3s - loss: 0.0062 - acc: 0.9996 - val_loss: 0.0597 - val_acc: 0.9820 Epoch 24/300 - 3s - loss: 0.0054 - acc: 0.9997 - val_loss: 0.0585 - val_acc: 0.9828 Epoch 25/300 - 3s - loss: 0.0049 - acc: 0.9998 - val_loss: 0.0593 - val_acc: 0.9822 Epoch 26/300 - 3s - loss: 0.0043 - acc: 0.9999 - val_loss: 0.0604 - val_acc: 0.9821 Epoch 27/300 - 3s - loss: 0.0039 - acc: 0.9999 - val_loss: 0.0606 - val_acc: 0.9819 Epoch 28/300 - 3s - loss: 0.0036 - acc: 0.9999 - val_loss: 0.0598 - val_acc: 0.9822 Epoch 29/300 - 3s - loss: 0.0032 - acc: 0.9999 - val_loss: 0.0599 - val_acc: 0.9829 Epoch 30/300 - 3s - loss: 0.0029 - acc: 1.0000 - val_loss: 0.0608 - val_acc: 0.9818 Epoch 31/300 - 3s - loss: 0.0027 - acc: 1.0000 - val_loss: 0.0615 - val_acc: 0.9821 Epoch 32/300 - 3s - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0612 - val_acc: 0.9823 Epoch 33/300 - 3s - loss: 0.0023 - acc: 1.0000 - val_loss: 0.0623 - val_acc: 0.9816 Epoch 34/300 - 3s - loss: 0.0020 - acc: 1.0000 - val_loss: 0.0611 - val_acc: 0.9825 Epoch 35/300 - 3s - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0622 - val_acc: 0.9823 Epoch 36/300 - 3s - loss: 0.0017 - acc: 1.0000 - val_loss: 0.0624 - val_acc: 0.9825 Epoch 37/300 - 3s - loss: 0.0016 - acc: 1.0000 - val_loss: 0.0625 - val_acc: 0.9823 Epoch 38/300 - 3s - loss: 0.0015 - acc: 1.0000 - val_loss: 0.0625 - val_acc: 0.9827 Epoch 39/300 - 3s - loss: 0.0014 - acc: 1.0000 - val_loss: 0.0639 - val_acc: 0.9822 Epoch 40/300 - 3s - loss: 0.0013 - acc: 1.0000 - val_loss: 0.0632 - val_acc: 0.9832 Epoch 41/300 - 3s - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0638 - val_acc: 0.9827 Epoch 42/300 - 3s - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0632 - val_acc: 0.9830 Epoch 43/300 - 3s - loss: 0.0011 - acc: 1.0000 - val_loss: 0.0647 - val_acc: 0.9824 Epoch 44/300 - 3s - loss: 0.0010 - acc: 1.0000 - val_loss: 0.0643 - val_acc: 0.9827 Epoch 45/300 - 3s - loss: 9.6194e-04 - acc: 1.0000 - val_loss: 0.0647 - val_acc: 0.9825 Epoch 46/300 - 3s - loss: 9.0778e-04 - acc: 1.0000 - val_loss: 0.0649 - val_acc: 0.9828 Epoch 47/300 - 3s - loss: 8.3689e-04 - acc: 1.0000 - val_loss: 0.0655 - val_acc: 0.9826 Epoch 48/300 - 3s - loss: 8.0184e-04 - acc: 1.0000 - val_loss: 0.0653 - val_acc: 0.9827 Epoch 49/300 - 3s - loss: 7.5783e-04 - acc: 1.0000 - val_loss: 0.0654 - val_acc: 0.9829 Epoch 50/300 - 3s - loss: 7.0835e-04 - acc: 1.0000 - val_loss: 0.0662 - val_acc: 0.9829 Epoch 51/300 - 3s - loss: 6.7129e-04 - acc: 1.0000 - val_loss: 0.0663 - val_acc: 0.9829 Epoch 52/300 - 3s - loss: 6.4625e-04 - acc: 1.0000 - val_loss: 0.0665 - val_acc: 0.9834 Epoch 53/300 - 3s - loss: 6.0120e-04 - acc: 1.0000 - val_loss: 0.0664 - val_acc: 0.9830 Epoch 54/300 - 3s - loss: 5.8912e-04 - acc: 1.0000 - val_loss: 0.0668 - val_acc: 0.9825 Epoch 55/300 - 3s - loss: 5.4232e-04 - acc: 1.0000 - val_loss: 0.0676 - val_acc: 0.9826 Epoch 56/300 - 3s - loss: 5.1240e-04 - acc: 1.0000 - val_loss: 0.0674 - val_acc: 0.9825 Epoch 57/300 - 3s - loss: 4.8981e-04 - acc: 1.0000 - val_loss: 0.0678 - val_acc: 0.9831 Epoch 58/300 - 3s - loss: 4.6388e-04 - acc: 1.0000 - val_loss: 0.0675 - val_acc: 0.9833 Epoch 59/300 - 3s - loss: 4.4094e-04 - acc: 1.0000 - val_loss: 0.0680 - val_acc: 0.9829 Epoch 60/300 - 3s - loss: 4.2229e-04 - acc: 1.0000 - val_loss: 0.0683 - val_acc: 0.9832 Epoch 61/300 - 3s - loss: 4.0432e-04 - acc: 1.0000 - val_loss: 0.0681 - val_acc: 0.9832 Epoch 62/300 - 3s - loss: 3.8754e-04 - acc: 1.0000 - val_loss: 0.0689 - val_acc: 0.9824 Epoch 63/300 - 3s - loss: 3.6442e-04 - acc: 1.0000 - val_loss: 0.0687 - val_acc: 0.9826 Epoch 64/300 - 3s - loss: 3.4488e-04 - acc: 1.0000 - val_loss: 0.0694 - val_acc: 0.9830 Epoch 65/300 - 3s - loss: 3.3590e-04 - acc: 1.0000 - val_loss: 0.0694 - val_acc: 0.9829 Epoch 66/300 - 3s - loss: 3.1757e-04 - acc: 1.0000 - val_loss: 0.0698 - val_acc: 0.9824 Epoch 67/300 - 3s - loss: 3.0166e-04 - acc: 1.0000 - val_loss: 0.0702 - val_acc: 0.9830 Epoch 68/300 - 3s - loss: 2.8783e-04 - acc: 1.0000 - val_loss: 0.0699 - val_acc: 0.9829 Epoch 69/300 - 3s - loss: 2.7600e-04 - acc: 1.0000 - val_loss: 0.0699 - val_acc: 0.9830 Epoch 70/300 - 3s - loss: 2.6438e-04 - acc: 1.0000 - val_loss: 0.0706 - val_acc: 0.9828 Epoch 71/300 - 3s - loss: 2.5033e-04 - acc: 1.0000 - val_loss: 0.0706 - val_acc: 0.9828 Epoch 72/300 - 3s - loss: 2.4264e-04 - acc: 1.0000 - val_loss: 0.0717 - val_acc: 0.9828 Epoch 73/300 - 3s - loss: 2.3041e-04 - acc: 1.0000 - val_loss: 0.0713 - val_acc: 0.9830 Epoch 74/300 - 3s - loss: 2.2231e-04 - acc: 1.0000 - val_loss: 0.0716 - val_acc: 0.9830 Epoch 75/300 - 3s - loss: 2.1054e-04 - acc: 1.0000 - val_loss: 0.0719 - val_acc: 0.9832 Epoch 76/300 - 3s - loss: 2.0212e-04 - acc: 1.0000 - val_loss: 0.0715 - val_acc: 0.9832 Epoch 77/300 - 3s - loss: 1.9441e-04 - acc: 1.0000 - val_loss: 0.0718 - val_acc: 0.9834 Epoch 78/300 - 3s - loss: 1.8792e-04 - acc: 1.0000 - val_loss: 0.0721 - val_acc: 0.9831 Epoch 79/300 - 3s - loss: 1.7854e-04 - acc: 1.0000 - val_loss: 0.0725 - val_acc: 0.9830 Epoch 80/300 - 3s - loss: 1.7123e-04 - acc: 1.0000 - val_loss: 0.0730 - val_acc: 0.9830 Epoch 81/300 - 3s - loss: 1.6479e-04 - acc: 1.0000 - val_loss: 0.0732 - val_acc: 0.9833 Epoch 82/300 - 3s - loss: 1.5884e-04 - acc: 1.0000 - val_loss: 0.0733 - val_acc: 0.9832 Epoch 83/300 - 3s - loss: 1.5171e-04 - acc: 1.0000 - val_loss: 0.0732 - val_acc: 0.9830 Epoch 84/300 - 3s - loss: 1.4709e-04 - acc: 1.0000 - val_loss: 0.0736 - val_acc: 0.9833 Epoch 85/300 - 3s - loss: 1.3951e-04 - acc: 1.0000 - val_loss: 0.0739 - val_acc: 0.9832 Epoch 86/300 - 3s - loss: 1.3498e-04 - acc: 1.0000 - val_loss: 0.0739 - val_acc: 0.9831 Epoch 87/300 - 3s - loss: 1.2849e-04 - acc: 1.0000 - val_loss: 0.0740 - val_acc: 0.9830 Epoch 88/300 - 3s - loss: 1.2299e-04 - acc: 1.0000 - val_loss: 0.0747 - val_acc: 0.9831 Epoch 89/300 - 3s - loss: 1.1987e-04 - acc: 1.0000 - val_loss: 0.0745 - val_acc: 0.9830 Epoch 90/300 - 3s - loss: 1.1488e-04 - acc: 1.0000 - val_loss: 0.0749 - val_acc: 0.9831 Epoch 91/300 - 3s - loss: 1.0998e-04 - acc: 1.0000 - val_loss: 0.0754 - val_acc: 0.9833 Epoch 92/300 - 3s - loss: 1.0693e-04 - acc: 1.0000 - val_loss: 0.0753 - val_acc: 0.9831 Epoch 93/300 - 3s - loss: 1.0226e-04 - acc: 1.0000 - val_loss: 0.0754 - val_acc: 0.9832 Epoch 94/300 - 3s - loss: 9.8189e-05 - acc: 1.0000 - val_loss: 0.0756 - val_acc: 0.9834 Epoch 95/300 - 3s - loss: 9.4611e-05 - acc: 1.0000 - val_loss: 0.0755 - val_acc: 0.9830 Epoch 96/300 - 3s - loss: 9.1917e-05 - acc: 1.0000 - val_loss: 0.0766 - val_acc: 0.9833 Epoch 97/300 - 3s - loss: 8.7415e-05 - acc: 1.0000 - val_loss: 0.0763 - val_acc: 0.9833 Epoch 98/300 - 3s - loss: 8.4045e-05 - acc: 1.0000 - val_loss: 0.0766 - val_acc: 0.9829 Epoch 99/300 - 3s - loss: 8.0744e-05 - acc: 1.0000 - val_loss: 0.0767 - val_acc: 0.9828 Epoch 100/300 - 3s - loss: 7.8602e-05 - acc: 1.0000 - val_loss: 0.0767 - val_acc: 0.9831 Epoch 101/300 - 3s - loss: 7.5436e-05 - acc: 1.0000 - val_loss: 0.0775 - val_acc: 0.9829 Epoch 102/300 - 3s - loss: 7.2617e-05 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9833 Epoch 103/300 - 3s - loss: 6.9956e-05 - acc: 1.0000 - val_loss: 0.0776 - val_acc: 0.9834 Epoch 104/300 - 3s - loss: 6.7348e-05 - acc: 1.0000 - val_loss: 0.0775 - val_acc: 0.9835 Epoch 105/300 - 3s - loss: 6.4409e-05 - acc: 1.0000 - val_loss: 0.0780 - val_acc: 0.9835 Epoch 106/300 - 3s - loss: 6.2338e-05 - acc: 1.0000 - val_loss: 0.0779 - val_acc: 0.9834 Epoch 107/300 - 3s - loss: 6.0230e-05 - acc: 1.0000 - val_loss: 0.0787 - val_acc: 0.9834 Epoch 108/300 - 3s - loss: 5.7820e-05 - acc: 1.0000 - val_loss: 0.0789 - val_acc: 0.9834 Epoch 109/300 - 3s - loss: 5.5781e-05 - acc: 1.0000 - val_loss: 0.0790 - val_acc: 0.9833 Epoch 110/300 - 3s - loss: 5.3571e-05 - acc: 1.0000 - val_loss: 0.0788 - val_acc: 0.9833 Epoch 111/300 - 3s - loss: 5.1880e-05 - acc: 1.0000 - val_loss: 0.0794 - val_acc: 0.9832 Epoch 112/300 - 3s - loss: 4.9978e-05 - acc: 1.0000 - val_loss: 0.0794 - val_acc: 0.9833 Epoch 113/300 - 3s - loss: 4.7937e-05 - acc: 1.0000 - val_loss: 0.0796 - val_acc: 0.9833 Epoch 114/300 - 3s - loss: 4.6254e-05 - acc: 1.0000 - val_loss: 0.0795 - val_acc: 0.9830 Epoch 115/300 - 3s - loss: 4.4681e-05 - acc: 1.0000 - val_loss: 0.0793 - val_acc: 0.9836 Epoch 116/300 - 3s - loss: 4.3043e-05 - acc: 1.0000 - val_loss: 0.0802 - val_acc: 0.9832 Epoch 117/300 - 3s - loss: 4.1942e-05 - acc: 1.0000 - val_loss: 0.0800 - val_acc: 0.9831 Epoch 118/300 - 3s - loss: 4.0442e-05 - acc: 1.0000 - val_loss: 0.0804 - val_acc: 0.9834 Epoch 119/300 - 3s - loss: 3.8788e-05 - acc: 1.0000 - val_loss: 0.0805 - val_acc: 0.9836 Epoch 120/300 - 3s - loss: 3.7390e-05 - acc: 1.0000 - val_loss: 0.0810 - val_acc: 0.9832 Epoch 121/300 - 3s - loss: 3.5820e-05 - acc: 1.0000 - val_loss: 0.0813 - val_acc: 0.9835 Epoch 122/300 - 3s - loss: 3.4850e-05 - acc: 1.0000 - val_loss: 0.0814 - val_acc: 0.9832 Epoch 123/300 - 3s - loss: 3.3479e-05 - acc: 1.0000 - val_loss: 0.0809 - val_acc: 0.9834 Epoch 124/300 - 3s - loss: 3.2486e-05 - acc: 1.0000 - val_loss: 0.0818 - val_acc: 0.9836 Epoch 125/300 - 3s - loss: 3.1350e-05 - acc: 1.0000 - val_loss: 0.0816 - val_acc: 0.9832 Epoch 126/300 - 3s - loss: 3.0118e-05 - acc: 1.0000 - val_loss: 0.0823 - val_acc: 0.9834 Epoch 127/300 - 3s - loss: 2.9347e-05 - acc: 1.0000 - val_loss: 0.0815 - val_acc: 0.9834 Epoch 128/300 - 3s - loss: 2.8244e-05 - acc: 1.0000 - val_loss: 0.0824 - val_acc: 0.9833 Epoch 129/300 - 3s - loss: 2.7450e-05 - acc: 1.0000 - val_loss: 0.0825 - val_acc: 0.9834 Epoch 130/300 - 3s - loss: 2.6489e-05 - acc: 1.0000 - val_loss: 0.0824 - val_acc: 0.9835 Epoch 131/300 - 3s - loss: 2.5162e-05 - acc: 1.0000 - val_loss: 0.0826 - val_acc: 0.9834 Epoch 132/300 - 3s - loss: 2.4514e-05 - acc: 1.0000 - val_loss: 0.0831 - val_acc: 0.9835 Epoch 133/300 - 3s - loss: 2.3663e-05 - acc: 1.0000 - val_loss: 0.0831 - val_acc: 0.9834 Epoch 134/300 - 3s - loss: 2.2848e-05 - acc: 1.0000 - val_loss: 0.0835 - val_acc: 0.9836 Epoch 135/300 - 3s - loss: 2.2102e-05 - acc: 1.0000 - val_loss: 0.0831 - val_acc: 0.9834 Epoch 136/300 - 3s - loss: 2.1387e-05 - acc: 1.0000 - val_loss: 0.0837 - val_acc: 0.9835 Epoch 137/300 - 3s - loss: 2.0636e-05 - acc: 1.0000 - val_loss: 0.0836 - val_acc: 0.9836 Epoch 138/300 - 3s - loss: 1.9981e-05 - acc: 1.0000 - val_loss: 0.0840 - val_acc: 0.9833 Epoch 139/300 - 3s - loss: 1.9222e-05 - acc: 1.0000 - val_loss: 0.0841 - val_acc: 0.9837 Epoch 140/300 - 3s - loss: 1.8659e-05 - acc: 1.0000 - val_loss: 0.0841 - val_acc: 0.9836 Epoch 141/300 - 3s - loss: 1.7933e-05 - acc: 1.0000 - val_loss: 0.0849 - val_acc: 0.9836 Epoch 142/300 - 3s - loss: 1.7273e-05 - acc: 1.0000 - val_loss: 0.0847 - val_acc: 0.9833 Epoch 143/300 - 3s - loss: 1.6808e-05 - acc: 1.0000 - val_loss: 0.0850 - val_acc: 0.9837 Epoch 144/300 - 3s - loss: 1.6208e-05 - acc: 1.0000 - val_loss: 0.0854 - val_acc: 0.9837 Epoch 145/300 - 3s - loss: 1.5627e-05 - acc: 1.0000 - val_loss: 0.0851 - val_acc: 0.9838 Epoch 146/300 - 3s - loss: 1.5067e-05 - acc: 1.0000 - val_loss: 0.0855 - val_acc: 0.9837 Epoch 147/300 - 3s - loss: 1.4659e-05 - acc: 1.0000 - val_loss: 0.0857 - val_acc: 0.9835 Epoch 148/300 - 3s - loss: 1.4082e-05 - acc: 1.0000 - val_loss: 0.0865 - val_acc: 0.9835 Epoch 149/300 - 3s - loss: 1.3644e-05 - acc: 1.0000 - val_loss: 0.0866 - val_acc: 0.9835 Epoch 150/300 - 3s - loss: 1.3211e-05 - acc: 1.0000 - val_loss: 0.0863 - val_acc: 0.9836 Epoch 151/300 - 3s - loss: 1.2773e-05 - acc: 1.0000 - val_loss: 0.0864 - val_acc: 0.9834 Epoch 152/300 - 3s - loss: 1.2374e-05 - acc: 1.0000 - val_loss: 0.0865 - val_acc: 0.9837 Epoch 153/300 - 3s - loss: 1.1972e-05 - acc: 1.0000 - val_loss: 0.0865 - val_acc: 0.9838 Epoch 154/300 - 3s - loss: 1.1606e-05 - acc: 1.0000 - val_loss: 0.0870 - val_acc: 0.9837 Epoch 155/300 - 3s - loss: 1.1133e-05 - acc: 1.0000 - val_loss: 0.0871 - val_acc: 0.9837 Epoch 156/300 - 3s - loss: 1.0725e-05 - acc: 1.0000 - val_loss: 0.0874 - val_acc: 0.9835 Epoch 157/300 - 3s - loss: 1.0437e-05 - acc: 1.0000 - val_loss: 0.0876 - val_acc: 0.9836 Epoch 158/300 - 3s - loss: 1.0004e-05 - acc: 1.0000 - val_loss: 0.0877 - val_acc: 0.9835 Epoch 159/300 - 3s - loss: 9.7196e-06 - acc: 1.0000 - val_loss: 0.0878 - val_acc: 0.9837 Epoch 160/300 - 3s - loss: 9.4301e-06 - acc: 1.0000 - val_loss: 0.0880 - val_acc: 0.9837 Epoch 161/300 - 3s - loss: 9.1314e-06 - acc: 1.0000 - val_loss: 0.0879 - val_acc: 0.9835 Epoch 162/300 - 3s - loss: 8.8318e-06 - acc: 1.0000 - val_loss: 0.0882 - val_acc: 0.9835 Epoch 163/300 - 3s - loss: 8.5024e-06 - acc: 1.0000 - val_loss: 0.0882 - val_acc: 0.9836 Epoch 164/300 - 3s - loss: 8.2271e-06 - acc: 1.0000 - val_loss: 0.0885 - val_acc: 0.9836 Epoch 165/300 - 3s - loss: 7.9456e-06 - acc: 1.0000 - val_loss: 0.0889 - val_acc: 0.9835 Epoch 166/300 - 3s - loss: 7.7393e-06 - acc: 1.0000 - val_loss: 0.0892 - val_acc: 0.9837 Epoch 167/300 - 3s - loss: 7.5925e-06 - acc: 1.0000 - val_loss: 0.0891 - val_acc: 0.9839 Epoch 168/300 - 3s - loss: 7.2755e-06 - acc: 1.0000 - val_loss: 0.0894 - val_acc: 0.9836 Epoch 169/300 - 3s - loss: 7.0094e-06 - acc: 1.0000 - val_loss: 0.0895 - val_acc: 0.9836 Epoch 170/300 - 3s - loss: 6.7810e-06 - acc: 1.0000 - val_loss: 0.0899 - val_acc: 0.9838 Epoch 171/300 - 3s - loss: 6.5739e-06 - acc: 1.0000 - val_loss: 0.0898 - val_acc: 0.9834 Epoch 172/300 - 3s - loss: 6.3367e-06 - acc: 1.0000 - val_loss: 0.0898 - val_acc: 0.9836 Epoch 173/300 - 3s - loss: 6.1390e-06 - acc: 1.0000 - val_loss: 0.0899 - val_acc: 0.9837 Epoch 174/300 - 4s - loss: 5.9376e-06 - acc: 1.0000 - val_loss: 0.0902 - val_acc: 0.9833 Epoch 175/300 - 3s - loss: 5.8013e-06 - acc: 1.0000 - val_loss: 0.0901 - val_acc: 0.9837 Epoch 176/300 - 3s - loss: 5.5484e-06 - acc: 1.0000 - val_loss: 0.0908 - val_acc: 0.9838 Epoch 177/300 - 3s - loss: 5.4190e-06 - acc: 1.0000 - val_loss: 0.0906 - val_acc: 0.9837 Epoch 178/300 - 3s - loss: 5.2406e-06 - acc: 1.0000 - val_loss: 0.0909 - val_acc: 0.9838 Epoch 179/300 - 3s - loss: 5.0728e-06 - acc: 1.0000 - val_loss: 0.0912 - val_acc: 0.9837 Epoch 180/300 - 3s - loss: 4.9085e-06 - acc: 1.0000 - val_loss: 0.0911 - val_acc: 0.9837 Epoch 181/300 - 3s - loss: 4.7424e-06 - acc: 1.0000 - val_loss: 0.0913 - val_acc: 0.9839 Epoch 182/300 - 3s - loss: 4.5958e-06 - acc: 1.0000 - val_loss: 0.0914 - val_acc: 0.9838 Epoch 183/300 - 3s - loss: 4.4556e-06 - acc: 1.0000 - val_loss: 0.0920 - val_acc: 0.9838 Epoch 184/300 - 3s - loss: 4.3136e-06 - acc: 1.0000 - val_loss: 0.0920 - val_acc: 0.9837 Epoch 185/300 - 3s - loss: 4.1718e-06 - acc: 1.0000 - val_loss: 0.0917 - val_acc: 0.9837 Epoch 186/300 - 3s - loss: 4.0398e-06 - acc: 1.0000 - val_loss: 0.0921 - val_acc: 0.9835 Epoch 187/300 - 3s - loss: 3.9448e-06 - acc: 1.0000 - val_loss: 0.0923 - val_acc: 0.9837 Epoch 188/300 - 3s - loss: 3.7964e-06 - acc: 1.0000 - val_loss: 0.0923 - val_acc: 0.9839 Epoch 189/300 - 3s - loss: 3.6852e-06 - acc: 1.0000 - val_loss: 0.0925 - val_acc: 0.9837 Epoch 190/300 - 3s - loss: 3.5655e-06 - acc: 1.0000 - val_loss: 0.0928 - val_acc: 0.9838 Epoch 191/300 - 3s - loss: 3.4688e-06 - acc: 1.0000 - val_loss: 0.0928 - val_acc: 0.9837 Epoch 192/300 - 3s - loss: 3.3602e-06 - acc: 1.0000 - val_loss: 0.0931 - val_acc: 0.9837 Epoch 193/300 - 3s - loss: 3.2580e-06 - acc: 1.0000 - val_loss: 0.0931 - val_acc: 0.9835 Epoch 194/300 - 3s - loss: 3.1606e-06 - acc: 1.0000 - val_loss: 0.0930 - val_acc: 0.9834 Epoch 195/300 - 3s - loss: 3.0486e-06 - acc: 1.0000 - val_loss: 0.0934 - val_acc: 0.9836 Epoch 196/300 - 3s - loss: 2.9646e-06 - acc: 1.0000 - val_loss: 0.0936 - val_acc: 0.9838 Epoch 197/300 - 3s - loss: 2.8880e-06 - acc: 1.0000 - val_loss: 0.0940 - val_acc: 0.9837 Epoch 198/300 - 3s - loss: 2.8002e-06 - acc: 1.0000 - val_loss: 0.0937 - val_acc: 0.9837 Epoch 199/300 - 3s - loss: 2.7058e-06 - acc: 1.0000 - val_loss: 0.0940 - val_acc: 0.9836 Epoch 200/300 - 3s - loss: 2.6190e-06 - acc: 1.0000 - val_loss: 0.0940 - val_acc: 0.9836 Epoch 201/300 - 3s - loss: 2.5547e-06 - acc: 1.0000 - val_loss: 0.0945 - val_acc: 0.9835 Epoch 202/300 - 3s - loss: 2.4747e-06 - acc: 1.0000 - val_loss: 0.0946 - val_acc: 0.9838 Epoch 203/300 - 3s - loss: 2.3913e-06 - acc: 1.0000 - val_loss: 0.0949 - val_acc: 0.9837 Epoch 204/300 - 3s - loss: 2.3268e-06 - acc: 1.0000 - val_loss: 0.0946 - val_acc: 0.9836 Epoch 205/300 - 3s - loss: 2.2548e-06 - acc: 1.0000 - val_loss: 0.0947 - val_acc: 0.9836 Epoch 206/300 - 3s - loss: 2.1882e-06 - acc: 1.0000 - val_loss: 0.0948 - val_acc: 0.9835 Epoch 207/300 - 3s - loss: 2.1258e-06 - acc: 1.0000 - val_loss: 0.0951 - val_acc: 0.9835 Epoch 208/300 - 3s - loss: 2.0708e-06 - acc: 1.0000 - val_loss: 0.0954 - val_acc: 0.9834 Epoch 209/300 - 3s - loss: 2.0087e-06 - acc: 1.0000 - val_loss: 0.0956 - val_acc: 0.9836 Epoch 210/300 - 3s - loss: 1.9515e-06 - acc: 1.0000 - val_loss: 0.0957 - val_acc: 0.9837 Epoch 211/300 - 3s - loss: 1.8939e-06 - acc: 1.0000 - val_loss: 0.0955 - val_acc: 0.9838 Epoch 212/300 - 3s - loss: 1.8318e-06 - acc: 1.0000 - val_loss: 0.0961 - val_acc: 0.9838 Epoch 213/300 - 3s - loss: 1.7768e-06 - acc: 1.0000 - val_loss: 0.0965 - val_acc: 0.9838 Epoch 214/300 - 3s - loss: 1.7453e-06 - acc: 1.0000 - val_loss: 0.0965 - val_acc: 0.9837 Epoch 215/300 - 3s - loss: 1.6773e-06 - acc: 1.0000 - val_loss: 0.0963 - val_acc: 0.9834 Epoch 216/300 - 3s - loss: 1.6451e-06 - acc: 1.0000 - val_loss: 0.0964 - val_acc: 0.9837 Epoch 217/300 - 3s - loss: 1.5821e-06 - acc: 1.0000 - val_loss: 0.0966 - val_acc: 0.9836 Epoch 218/300 - 3s - loss: 1.5421e-06 - acc: 1.0000 - val_loss: 0.0968 - val_acc: 0.9835 Epoch 219/300 - 3s - loss: 1.5006e-06 - acc: 1.0000 - val_loss: 0.0968 - val_acc: 0.9838 Epoch 220/300 - 3s - loss: 1.4623e-06 - acc: 1.0000 - val_loss: 0.0972 - val_acc: 0.9836 Epoch 221/300 - 3s - loss: 1.4146e-06 - acc: 1.0000 - val_loss: 0.0971 - val_acc: 0.9837 Epoch 222/300 - 3s - loss: 1.3827e-06 - acc: 1.0000 - val_loss: 0.0972 - val_acc: 0.9837 Epoch 223/300 - 3s - loss: 1.3375e-06 - acc: 1.0000 - val_loss: 0.0975 - val_acc: 0.9837 Epoch 224/300 - 3s - loss: 1.3092e-06 - acc: 1.0000 - val_loss: 0.0977 - val_acc: 0.9838 Epoch 225/300 - 3s - loss: 1.2728e-06 - acc: 1.0000 - val_loss: 0.0977 - val_acc: 0.9839 Epoch 226/300 - 3s - loss: 1.2347e-06 - acc: 1.0000 - val_loss: 0.0978 - val_acc: 0.9834 Epoch 227/300 - 3s - loss: 1.1935e-06 - acc: 1.0000 - val_loss: 0.0983 - val_acc: 0.9837 Epoch 228/300 - 3s - loss: 1.1687e-06 - acc: 1.0000 - val_loss: 0.0985 - val_acc: 0.9838 Epoch 229/300 - 3s - loss: 1.1407e-06 - acc: 1.0000 - val_loss: 0.0983 - val_acc: 0.9839 Epoch 230/300 - 3s - loss: 1.1039e-06 - acc: 1.0000 - val_loss: 0.0986 - val_acc: 0.9834 Epoch 231/300 - 3s - loss: 1.0760e-06 - acc: 1.0000 - val_loss: 0.0987 - val_acc: 0.9839 Epoch 232/300 - 3s - loss: 1.0546e-06 - acc: 1.0000 - val_loss: 0.0987 - val_acc: 0.9837 Epoch 233/300 - 3s - loss: 1.0228e-06 - acc: 1.0000 - val_loss: 0.0989 - val_acc: 0.9835 Epoch 234/300 - 3s - loss: 9.9359e-07 - acc: 1.0000 - val_loss: 0.0990 - val_acc: 0.9836 Epoch 235/300 - 3s - loss: 9.6863e-07 - acc: 1.0000 - val_loss: 0.0993 - val_acc: 0.9838 Epoch 236/300 - 3s - loss: 9.4514e-07 - acc: 1.0000 - val_loss: 0.0993 - val_acc: 0.9837 Epoch 237/300 - 3s - loss: 9.2869e-07 - acc: 1.0000 - val_loss: 0.0993 - val_acc: 0.9836 Epoch 238/300 - 3s - loss: 8.9592e-07 - acc: 1.0000 - val_loss: 0.0995 - val_acc: 0.9836 Epoch 239/300 - 3s - loss: 8.7460e-07 - acc: 1.0000 - val_loss: 0.0996 - val_acc: 0.9835 Epoch 240/300 - 3s - loss: 8.5597e-07 - acc: 1.0000 - val_loss: 0.0997 - val_acc: 0.9838 Epoch 241/300 - 3s - loss: 8.3255e-07 - acc: 1.0000 - val_loss: 0.1000 - val_acc: 0.9836 Epoch 242/300 - 3s - loss: 8.1206e-07 - acc: 1.0000 - val_loss: 0.1000 - val_acc: 0.9836 Epoch 243/300 - 3s - loss: 7.9508e-07 - acc: 1.0000 - val_loss: 0.1005 - val_acc: 0.9838 Epoch 244/300 - 3s - loss: 7.7165e-07 - acc: 1.0000 - val_loss: 0.1003 - val_acc: 0.9837 Epoch 245/300 - 3s - loss: 7.5228e-07 - acc: 1.0000 - val_loss: 0.1007 - val_acc: 0.9839 Epoch 246/300 - 3s - loss: 7.3610e-07 - acc: 1.0000 - val_loss: 0.1003 - val_acc: 0.9836 Epoch 247/300 - 3s - loss: 7.1769e-07 - acc: 1.0000 - val_loss: 0.1007 - val_acc: 0.9837 Epoch 248/300 - 3s - loss: 6.9995e-07 - acc: 1.0000 - val_loss: 0.1010 - val_acc: 0.9839 Epoch 249/300 - 3s - loss: 6.8623e-07 - acc: 1.0000 - val_loss: 0.1009 - val_acc: 0.9837 Epoch 250/300 - 3s - loss: 6.6548e-07 - acc: 1.0000 - val_loss: 0.1012 - val_acc: 0.9834 Epoch 251/300 - 3s - loss: 6.5409e-07 - acc: 1.0000 - val_loss: 0.1014 - val_acc: 0.9839 Epoch 252/300 - 3s - loss: 6.4120e-07 - acc: 1.0000 - val_loss: 0.1015 - val_acc: 0.9838 Epoch 253/300 - 3s - loss: 6.2190e-07 - acc: 1.0000 - val_loss: 0.1017 - val_acc: 0.9834 Epoch 254/300 - 3s - loss: 6.0925e-07 - acc: 1.0000 - val_loss: 0.1020 - val_acc: 0.9835 Epoch 255/300 - 3s - loss: 5.9585e-07 - acc: 1.0000 - val_loss: 0.1021 - val_acc: 0.9840 Epoch 256/300 - 3s - loss: 5.8089e-07 - acc: 1.0000 - val_loss: 0.1018 - val_acc: 0.9837 Epoch 257/300 - 3s - loss: 5.7196e-07 - acc: 1.0000 - val_loss: 0.1021 - val_acc: 0.9835 Epoch 258/300 - 3s - loss: 5.5789e-07 - acc: 1.0000 - val_loss: 0.1025 - val_acc: 0.9838 Epoch 259/300 - 3s - loss: 5.4679e-07 - acc: 1.0000 - val_loss: 0.1023 - val_acc: 0.9840 Epoch 260/300 - 3s - loss: 5.3333e-07 - acc: 1.0000 - val_loss: 0.1021 - val_acc: 0.9839 Epoch 261/300 - 3s - loss: 5.2341e-07 - acc: 1.0000 - val_loss: 0.1026 - val_acc: 0.9836 Epoch 262/300 - 3s - loss: 5.0919e-07 - acc: 1.0000 - val_loss: 0.1027 - val_acc: 0.9838 Epoch 263/300 - 3s - loss: 5.0080e-07 - acc: 1.0000 - val_loss: 0.1028 - val_acc: 0.9840 Epoch 264/300 - 3s - loss: 4.8936e-07 - acc: 1.0000 - val_loss: 0.1027 - val_acc: 0.9836 Epoch 265/300 - 3s - loss: 4.8162e-07 - acc: 1.0000 - val_loss: 0.1029 - val_acc: 0.9839 Epoch 266/300 - 3s - loss: 4.7047e-07 - acc: 1.0000 - val_loss: 0.1031 - val_acc: 0.9838 Epoch 267/300 - 3s - loss: 4.5974e-07 - acc: 1.0000 - val_loss: 0.1031 - val_acc: 0.9838 Epoch 268/300 - 3s - loss: 4.5105e-07 - acc: 1.0000 - val_loss: 0.1034 - val_acc: 0.9840 Epoch 269/300 - 3s - loss: 4.4195e-07 - acc: 1.0000 - val_loss: 0.1037 - val_acc: 0.9838 Epoch 270/300 - 3s - loss: 4.3360e-07 - acc: 1.0000 - val_loss: 0.1036 - val_acc: 0.9838 Epoch 271/300 - 3s - loss: 4.2704e-07 - acc: 1.0000 - val_loss: 0.1037 - val_acc: 0.9840 Epoch 272/300 - 3s - loss: 4.1747e-07 - acc: 1.0000 - val_loss: 0.1040 - val_acc: 0.9838 Epoch 273/300 - 3s - loss: 4.0963e-07 - acc: 1.0000 - val_loss: 0.1041 - val_acc: 0.9841 Epoch 274/300 - 3s - loss: 4.0081e-07 - acc: 1.0000 - val_loss: 0.1045 - val_acc: 0.9839 Epoch 275/300 - 3s - loss: 3.9514e-07 - acc: 1.0000 - val_loss: 0.1043 - val_acc: 0.9840 Epoch 276/300 - 3s - loss: 3.8734e-07 - acc: 1.0000 - val_loss: 0.1042 - val_acc: 0.9842 Epoch 277/300 - 3s - loss: 3.8035e-07 - acc: 1.0000 - val_loss: 0.1044 - val_acc: 0.9840 Epoch 278/300 - 3s - loss: 3.7277e-07 - acc: 1.0000 - val_loss: 0.1045 - val_acc: 0.9840 Epoch 279/300 - 3s - loss: 3.6763e-07 - acc: 1.0000 - val_loss: 0.1047 - val_acc: 0.9839 Epoch 280/300 - 3s - loss: 3.6084e-07 - acc: 1.0000 - val_loss: 0.1048 - val_acc: 0.9839 Epoch 281/300 - 3s - loss: 3.5480e-07 - acc: 1.0000 - val_loss: 0.1053 - val_acc: 0.9840 Epoch 282/300 - 3s - loss: 3.4911e-07 - acc: 1.0000 - val_loss: 0.1050 - val_acc: 0.9838 Epoch 283/300 - 3s - loss: 3.4319e-07 - acc: 1.0000 - val_loss: 0.1052 - val_acc: 0.9842 Epoch 284/300 - 3s - loss: 3.3739e-07 - acc: 1.0000 - val_loss: 0.1056 - val_acc: 0.9838 Epoch 285/300 - 3s - loss: 3.3219e-07 - acc: 1.0000 - val_loss: 0.1056 - val_acc: 0.9839 Epoch 286/300 - 3s - loss: 3.2867e-07 - acc: 1.0000 - val_loss: 0.1054 - val_acc: 0.9838 Epoch 287/300 - 3s - loss: 3.2200e-07 - acc: 1.0000 - val_loss: 0.1055 - val_acc: 0.9841 Epoch 288/300 - 3s - loss: 3.1716e-07 - acc: 1.0000 - val_loss: 0.1058 - val_acc: 0.9839 Epoch 289/300 - 3s - loss: 3.1087e-07 - acc: 1.0000 - val_loss: 0.1058 - val_acc: 0.9840 Epoch 290/300 - 3s - loss: 3.0634e-07 - acc: 1.0000 - val_loss: 0.1059 - val_acc: 0.9842 Epoch 291/300 - 3s - loss: 3.0191e-07 - acc: 1.0000 - val_loss: 0.1063 - val_acc: 0.9838 Epoch 292/300 - 3s - loss: 2.9703e-07 - acc: 1.0000 - val_loss: 0.1063 - val_acc: 0.9838 Epoch 293/300 - 3s - loss: 2.9311e-07 - acc: 1.0000 - val_loss: 0.1062 - val_acc: 0.9837 Epoch 294/300 - 3s - loss: 2.8903e-07 - acc: 1.0000 - val_loss: 0.1064 - val_acc: 0.9840 Epoch 295/300 - 3s - loss: 2.8474e-07 - acc: 1.0000 - val_loss: 0.1066 - val_acc: 0.9840 Epoch 296/300 - 3s - loss: 2.8142e-07 - acc: 1.0000 - val_loss: 0.1065 - val_acc: 0.9839 Epoch 297/300 - 3s - loss: 2.7653e-07 - acc: 1.0000 - val_loss: 0.1070 - val_acc: 0.9839 Epoch 298/300 - 3s - loss: 2.7363e-07 - acc: 1.0000 - val_loss: 0.1070 - val_acc: 0.9841 Epoch 299/300 - 3s - loss: 2.6964e-07 - acc: 1.0000 - val_loss: 0.1071 - val_acc: 0.9838 Epoch 300/300 - 3s - loss: 2.6562e-07 - acc: 1.0000 - val_loss: 0.1069 - val_acc: 0.9839 Baseline Error: 1.61%
INTERPRET¶
In [0]:
# PLOT THE RESULTS OF THE MODEL
def plot_train_curve(history):
colors = ['#e66101','#fdb863','#b2abd2','#5e3c99']
accuracy = history.history['acc']
val_accuracy = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(accuracy))
with plt.style.context("ggplot"):
plt.figure(figsize=(8, 8/1.618))
plt.plot(epochs, accuracy, marker='o', c=colors[3], label='Training accuracy')
plt.plot(epochs, val_accuracy, c=colors[0], label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure(figsize=(8, 8/1.618))
plt.plot(epochs, loss, marker='o', c=colors[3], label='Training loss')
plt.plot(epochs, val_loss, c=colors[0], label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
plot_train_curve(history)
In [0]: