Deep Learning Hello World In Keras
Assignment 1-a: Deep Learning Hello World! (Baseline)
Objective: To be able to implement a basic MLP in Keras for MNIST Classification
Step 1: Taking care of the imports which includes numpy, datasets, models, layers, optimizers, and utils.
You will also be able to tell if your set-up is correct/complete.
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.models import model_from_json
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
from matplotlib import pyplot as plt
%matplotlib inline
Step 2: Set-up some constants to be utilized in the training/testing of the model
NB_EPOCH = 200
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10 # number of outputs = number of digits, i.e. 0,1,2,3,4,5,6,7,8,9
OPTIMIZER = SGD() # Stocastic Gradient Descent optimizer
N_HIDDEN = 128
VALIDATION_SPLIT=0.2 # how much TRAIN dataset is reserved for VALIDATION
np.random.seed(1983) # for reproducibility
Step 3: Load the MNIST Dataset which are shuffled and split between train and test sets <br>
- X_train is 60000 rows of 28x28 values
- X_test is 10000 rows of 28x28 values
(X_train, y_train), (X_test, y_test) = mnist.load_data()
plt.subplot(1, 4, 1)
plt.imshow(X_train[0]) # first train
plt.subplot(1, 4, 2)
plt.imshow(X_train[59999]) # last train
plt.subplot(1, 4, 3)
plt.imshow(X_test[0]) # first test
plt.subplot(1, 4, 4)
plt.imshow(X_test[9999]) # last test
<matplotlib.image.AxesImage at 0x153eb8a6588>
Step 4: Preprocess the input data by reshaping it, converting it to float, and normalizing it [0-1].
# reshape
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# normalize
X_train /= 255
X_test /= 255
print(X_train.shape, 'train samples')
print(X_test.shape, 'test samples')
(60000, 784) train samples
(10000, 784) test samples
Step 5: Convert class vectors to binary class matrices; One-Hot-Encoding (OHE)
Y_train = np_utils.to_categorical(y_train, NB_CLASSES)
Y_test = np_utils.to_categorical(y_test, NB_CLASSES)
Step 6: Create the model: Input:784 ==> Output:10 (with Softmax activation)
model = Sequential()
model.add(Dense(NB_CLASSES, input_shape=(784,)))
model.add(Activation('softmax'))
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_3 (Dense) (None, 10) 7850
_________________________________________________________________
activation_3 (Activation) (None, 10) 0
=================================================================
Total params: 7,850
Trainable params: 7,850
Non-trainable params: 0
_________________________________________________________________
Step 7: Compile the model with categorical_crossentropy loss function, SGD optimizer, and accuracy metric
model.compile(loss='categorical_crossentropy',
optimizer=OPTIMIZER,
metrics=['accuracy'])
Step 8: Perform the training with 128 batch size, 200 epochs, and 20 % of the train data used for validation
history = model.fit(X_train, Y_train,
batch_size=BATCH_SIZE, epochs=NB_EPOCH,
verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
Train on 48000 samples, validate on 12000 samples
Epoch 1/200
48000/48000 [==============================] - 1s - loss: 1.4054 - acc: 0.6486 - val_loss: 0.9016 - val_acc: 0.8237
Epoch 2/200
48000/48000 [==============================] - 1s - loss: 0.7984 - acc: 0.8248 - val_loss: 0.6587 - val_acc: 0.8559
Epoch 3/200
48000/48000 [==============================] - 1s - loss: 0.6451 - acc: 0.8484 - val_loss: 0.5609 - val_acc: 0.8706
Epoch 4/200
48000/48000 [==============================] - 1s - loss: 0.5713 - acc: 0.8606 - val_loss: 0.5073 - val_acc: 0.8788
Epoch 5/200
48000/48000 [==============================] - 1s - loss: 0.5265 - acc: 0.8677 - val_loss: 0.4729 - val_acc: 0.8836
Epoch 6/200
48000/48000 [==============================] - 1s - loss: 0.4958 - acc: 0.8736 - val_loss: 0.4488 - val_acc: 0.8872
Epoch 7/200
48000/48000 [==============================] - 1s - loss: 0.4733 - acc: 0.8779 - val_loss: 0.4305 - val_acc: 0.8902
Epoch 8/200
48000/48000 [==============================] - 1s - loss: 0.4557 - acc: 0.8812 - val_loss: 0.4166 - val_acc: 0.8918
Epoch 9/200
48000/48000 [==============================] - 1s - loss: 0.4417 - acc: 0.8840 - val_loss: 0.4049 - val_acc: 0.8938
Epoch 10/200
48000/48000 [==============================] - 1s - loss: 0.4301 - acc: 0.8862 - val_loss: 0.3954 - val_acc: 0.8950
Epoch 11/200
48000/48000 [==============================] - 1s - loss: 0.4203 - acc: 0.8885 - val_loss: 0.3873 - val_acc: 0.89720.88
Epoch 12/200
48000/48000 [==============================] - 1s - loss: 0.4119 - acc: 0.8899 - val_loss: 0.3804 - val_acc: 0.8987
Epoch 13/200
48000/48000 [==============================] - 1s - loss: 0.4045 - acc: 0.8913 - val_loss: 0.3743 - val_acc: 0.9004
Epoch 14/200
48000/48000 [==============================] - 1s - loss: 0.3980 - acc: 0.8929 - val_loss: 0.3692 - val_acc: 0.9008
Epoch 15/200
48000/48000 [==============================] - 1s - loss: 0.3923 - acc: 0.8937 - val_loss: 0.3645 - val_acc: 0.9023
Epoch 16/200
48000/48000 [==============================] - 1s - loss: 0.3872 - acc: 0.8949 - val_loss: 0.3601 - val_acc: 0.9046
Epoch 17/200
48000/48000 [==============================] - 1s - loss: 0.3824 - acc: 0.8959 - val_loss: 0.3563 - val_acc: 0.9040
Epoch 18/200
48000/48000 [==============================] - 1s - loss: 0.3782 - acc: 0.8963 - val_loss: 0.3528 - val_acc: 0.9048
Epoch 19/200
48000/48000 [==============================] - 1s - loss: 0.3743 - acc: 0.8976 - val_loss: 0.3496 - val_acc: 0.9053
Epoch 20/200
48000/48000 [==============================] - 1s - loss: 0.3707 - acc: 0.8982 - val_loss: 0.3465 - val_acc: 0.9058
Epoch 21/200
48000/48000 [==============================] - 1s - loss: 0.3674 - acc: 0.8990 - val_loss: 0.3441 - val_acc: 0.9065
Epoch 22/200
48000/48000 [==============================] - 1s - loss: 0.3643 - acc: 0.8995 - val_loss: 0.3415 - val_acc: 0.9072
Epoch 23/200
48000/48000 [==============================] - 1s - loss: 0.3614 - acc: 0.8999 - val_loss: 0.3390 - val_acc: 0.9081
Epoch 24/200
48000/48000 [==============================] - 1s - loss: 0.3587 - acc: 0.9005 - val_loss: 0.3369 - val_acc: 0.9086
Epoch 25/200
48000/48000 [==============================] - 1s - loss: 0.3562 - acc: 0.9015 - val_loss: 0.3349 - val_acc: 0.9085
Epoch 26/200
48000/48000 [==============================] - 1s - loss: 0.3539 - acc: 0.9020 - val_loss: 0.3330 - val_acc: 0.9087
Epoch 27/200
48000/48000 [==============================] - 1s - loss: 0.3516 - acc: 0.9026 - val_loss: 0.3310 - val_acc: 0.9088
Epoch 28/200
48000/48000 [==============================] - 1s - loss: 0.3495 - acc: 0.9032 - val_loss: 0.3294 - val_acc: 0.9091
Epoch 29/200
48000/48000 [==============================] - 1s - loss: 0.3475 - acc: 0.9037 - val_loss: 0.3278 - val_acc: 0.9092
Epoch 30/200
48000/48000 [==============================] - 1s - loss: 0.3456 - acc: 0.9044 - val_loss: 0.3263 - val_acc: 0.9097
Epoch 31/200
48000/48000 [==============================] - 1s - loss: 0.3438 - acc: 0.9047 - val_loss: 0.3248 - val_acc: 0.9098
Epoch 32/200
48000/48000 [==============================] - 1s - loss: 0.3421 - acc: 0.9052 - val_loss: 0.3234 - val_acc: 0.9103
Epoch 33/200
48000/48000 [==============================] - 1s - loss: 0.3405 - acc: 0.9054 - val_loss: 0.3221 - val_acc: 0.9105
Epoch 34/200
48000/48000 [==============================] - 1s - loss: 0.3388 - acc: 0.9059 - val_loss: 0.3208 - val_acc: 0.9107
Epoch 35/200
48000/48000 [==============================] - 1s - loss: 0.3374 - acc: 0.9065 - val_loss: 0.3197 - val_acc: 0.9112
Epoch 36/200
48000/48000 [==============================] - 1s - loss: 0.3359 - acc: 0.9069 - val_loss: 0.3185 - val_acc: 0.9115
Epoch 37/200
48000/48000 [==============================] - 1s - loss: 0.3346 - acc: 0.9071 - val_loss: 0.3174 - val_acc: 0.9115
Epoch 38/200
48000/48000 [==============================] - 1s - loss: 0.3332 - acc: 0.9073 - val_loss: 0.3164 - val_acc: 0.9118
Epoch 39/200
48000/48000 [==============================] - 1s - loss: 0.3319 - acc: 0.9078 - val_loss: 0.3153 - val_acc: 0.9123
Epoch 40/200
48000/48000 [==============================] - 1s - loss: 0.3307 - acc: 0.9079 - val_loss: 0.3144 - val_acc: 0.9123
Epoch 41/200
48000/48000 [==============================] - 1s - loss: 0.3295 - acc: 0.9085 - val_loss: 0.3135 - val_acc: 0.9125
Epoch 42/200
48000/48000 [==============================] - 1s - loss: 0.3284 - acc: 0.9086 - val_loss: 0.3125 - val_acc: 0.9128
Epoch 43/200
48000/48000 [==============================] - 1s - loss: 0.3273 - acc: 0.9087 - val_loss: 0.3117 - val_acc: 0.9127
Epoch 44/200
48000/48000 [==============================] - 1s - loss: 0.3262 - acc: 0.9093 - val_loss: 0.3109 - val_acc: 0.9128
Epoch 45/200
48000/48000 [==============================] - 1s - loss: 0.3252 - acc: 0.9094 - val_loss: 0.3100 - val_acc: 0.9131
Epoch 46/200
48000/48000 [==============================] - 1s - loss: 0.3242 - acc: 0.9098 - val_loss: 0.3092 - val_acc: 0.9139
Epoch 47/200
48000/48000 [==============================] - 1s - loss: 0.3233 - acc: 0.9097 - val_loss: 0.3086 - val_acc: 0.9132
Epoch 48/200
48000/48000 [==============================] - 1s - loss: 0.3223 - acc: 0.9101 - val_loss: 0.3078 - val_acc: 0.9144
Epoch 49/200
48000/48000 [==============================] - 1s - loss: 0.3214 - acc: 0.9108 - val_loss: 0.3072 - val_acc: 0.9137
Epoch 50/200
48000/48000 [==============================] - 1s - loss: 0.3206 - acc: 0.9107 - val_loss: 0.3064 - val_acc: 0.9139
Epoch 51/200
48000/48000 [==============================] - 1s - loss: 0.3197 - acc: 0.9109 - val_loss: 0.3059 - val_acc: 0.9141
Epoch 52/200
48000/48000 [==============================] - 1s - loss: 0.3189 - acc: 0.9112 - val_loss: 0.3052 - val_acc: 0.9147
Epoch 53/200
48000/48000 [==============================] - 1s - loss: 0.3181 - acc: 0.9114 - val_loss: 0.3045 - val_acc: 0.9148
Epoch 54/200
48000/48000 [==============================] - 1s - loss: 0.3173 - acc: 0.9117 - val_loss: 0.3039 - val_acc: 0.9148
Epoch 55/200
48000/48000 [==============================] - 1s - loss: 0.3165 - acc: 0.9120 - val_loss: 0.3033 - val_acc: 0.9153
Epoch 56/200
48000/48000 [==============================] - 1s - loss: 0.3158 - acc: 0.9122 - val_loss: 0.3027 - val_acc: 0.9147
Epoch 57/200
48000/48000 [==============================] - 1s - loss: 0.3150 - acc: 0.9124 - val_loss: 0.3023 - val_acc: 0.9159
Epoch 58/200
48000/48000 [==============================] - 1s - loss: 0.3143 - acc: 0.9125 - val_loss: 0.3018 - val_acc: 0.9152
Epoch 59/200
48000/48000 [==============================] - 1s - loss: 0.3137 - acc: 0.9126 - val_loss: 0.3011 - val_acc: 0.9155
Epoch 60/200
48000/48000 [==============================] - 1s - loss: 0.3130 - acc: 0.9131 - val_loss: 0.3006 - val_acc: 0.9157
Epoch 61/200
48000/48000 [==============================] - 1s - loss: 0.3124 - acc: 0.9135 - val_loss: 0.3001 - val_acc: 0.9161
Epoch 62/200
48000/48000 [==============================] - 1s - loss: 0.3117 - acc: 0.9135 - val_loss: 0.2997 - val_acc: 0.9157
Epoch 63/200
48000/48000 [==============================] - 1s - loss: 0.3111 - acc: 0.9134 - val_loss: 0.2992 - val_acc: 0.9163
Epoch 64/200
48000/48000 [==============================] - 1s - loss: 0.3105 - acc: 0.9139 - val_loss: 0.2988 - val_acc: 0.9167
Epoch 65/200
48000/48000 [==============================] - 1s - loss: 0.3098 - acc: 0.9140 - val_loss: 0.2983 - val_acc: 0.9162
Epoch 66/200
48000/48000 [==============================] - 1s - loss: 0.3093 - acc: 0.9140 - val_loss: 0.2978 - val_acc: 0.9171
Epoch 67/200
48000/48000 [==============================] - 1s - loss: 0.3088 - acc: 0.9143 - val_loss: 0.2974 - val_acc: 0.9169
Epoch 68/200
48000/48000 [==============================] - 1s - loss: 0.3082 - acc: 0.9141 - val_loss: 0.2970 - val_acc: 0.9172
Epoch 69/200
48000/48000 [==============================] - 1s - loss: 0.3076 - acc: 0.9144 - val_loss: 0.2967 - val_acc: 0.9172
Epoch 70/200
48000/48000 [==============================] - 1s - loss: 0.3071 - acc: 0.9148 - val_loss: 0.2963 - val_acc: 0.9176
Epoch 71/200
48000/48000 [==============================] - 1s - loss: 0.3066 - acc: 0.9147 - val_loss: 0.2958 - val_acc: 0.9170
Epoch 72/200
48000/48000 [==============================] - 1s - loss: 0.3060 - acc: 0.9149 - val_loss: 0.2955 - val_acc: 0.9177
Epoch 73/200
48000/48000 [==============================] - 1s - loss: 0.3056 - acc: 0.9148 - val_loss: 0.2951 - val_acc: 0.9175
Epoch 74/200
48000/48000 [==============================] - 1s - loss: 0.3051 - acc: 0.9150 - val_loss: 0.2947 - val_acc: 0.9175
Epoch 75/200
48000/48000 [==============================] - 1s - loss: 0.3046 - acc: 0.9154 - val_loss: 0.2943 - val_acc: 0.9175
Epoch 76/200
48000/48000 [==============================] - 1s - loss: 0.3041 - acc: 0.9154 - val_loss: 0.2939 - val_acc: 0.9180
Epoch 77/200
48000/48000 [==============================] - 1s - loss: 0.3037 - acc: 0.9157 - val_loss: 0.2936 - val_acc: 0.9182
Epoch 78/200
48000/48000 [==============================] - 1s - loss: 0.3032 - acc: 0.9156 - val_loss: 0.2933 - val_acc: 0.9182
Epoch 79/200
48000/48000 [==============================] - 1s - loss: 0.3027 - acc: 0.9160 - val_loss: 0.2930 - val_acc: 0.9184
Epoch 80/200
48000/48000 [==============================] - 1s - loss: 0.3023 - acc: 0.9160 - val_loss: 0.2926 - val_acc: 0.9184
Epoch 81/200
48000/48000 [==============================] - 1s - loss: 0.3019 - acc: 0.9163 - val_loss: 0.2924 - val_acc: 0.9184
Epoch 82/200
48000/48000 [==============================] - 1s - loss: 0.3014 - acc: 0.9160 - val_loss: 0.2920 - val_acc: 0.9187
Epoch 83/200
48000/48000 [==============================] - 1s - loss: 0.3010 - acc: 0.9162 - val_loss: 0.2917 - val_acc: 0.9188
Epoch 84/200
48000/48000 [==============================] - 1s - loss: 0.3006 - acc: 0.9166 - val_loss: 0.2915 - val_acc: 0.9184
Epoch 85/200
48000/48000 [==============================] - 1s - loss: 0.3002 - acc: 0.9165 - val_loss: 0.2912 - val_acc: 0.9185
Epoch 86/200
48000/48000 [==============================] - 1s - loss: 0.2999 - acc: 0.9167 - val_loss: 0.2909 - val_acc: 0.9186
Epoch 87/200
48000/48000 [==============================] - 1s - loss: 0.2994 - acc: 0.9165 - val_loss: 0.2906 - val_acc: 0.9188
Epoch 88/200
48000/48000 [==============================] - 1s - loss: 0.2990 - acc: 0.9165 - val_loss: 0.2904 - val_acc: 0.9188
Epoch 89/200
48000/48000 [==============================] - 1s - loss: 0.2987 - acc: 0.9169 - val_loss: 0.2900 - val_acc: 0.9187
Epoch 90/200
48000/48000 [==============================] - 1s - loss: 0.2983 - acc: 0.9173 - val_loss: 0.2898 - val_acc: 0.9192
Epoch 91/200
48000/48000 [==============================] - 1s - loss: 0.2979 - acc: 0.9170 - val_loss: 0.2895 - val_acc: 0.9192
Epoch 92/200
48000/48000 [==============================] - 1s - loss: 0.2976 - acc: 0.9172 - val_loss: 0.2893 - val_acc: 0.9192
Epoch 93/200
48000/48000 [==============================] - 1s - loss: 0.2973 - acc: 0.9173 - val_loss: 0.2890 - val_acc: 0.9192
Epoch 94/200
48000/48000 [==============================] - 1s - loss: 0.2968 - acc: 0.9177 - val_loss: 0.2889 - val_acc: 0.9193
Epoch 95/200
48000/48000 [==============================] - 1s - loss: 0.2965 - acc: 0.9173 - val_loss: 0.2886 - val_acc: 0.9198
Epoch 96/200
48000/48000 [==============================] - 1s - loss: 0.2962 - acc: 0.9175 - val_loss: 0.2883 - val_acc: 0.9194
Epoch 97/200
48000/48000 [==============================] - 1s - loss: 0.2959 - acc: 0.9178 - val_loss: 0.2880 - val_acc: 0.9195
Epoch 98/200
48000/48000 [==============================] - 1s - loss: 0.2956 - acc: 0.9179 - val_loss: 0.2878 - val_acc: 0.9196
Epoch 99/200
48000/48000 [==============================] - 1s - loss: 0.2952 - acc: 0.9178 - val_loss: 0.2877 - val_acc: 0.9201
Epoch 100/200
48000/48000 [==============================] - 1s - loss: 0.2949 - acc: 0.9179 - val_loss: 0.2874 - val_acc: 0.92080.917
Epoch 101/200
48000/48000 [==============================] - 1s - loss: 0.2946 - acc: 0.9180 - val_loss: 0.2871 - val_acc: 0.9200
Epoch 102/200
48000/48000 [==============================] - 1s - loss: 0.2943 - acc: 0.9179 - val_loss: 0.2869 - val_acc: 0.9199
Epoch 103/200
48000/48000 [==============================] - 1s - loss: 0.2939 - acc: 0.9180 - val_loss: 0.2868 - val_acc: 0.9199
Epoch 104/200
48000/48000 [==============================] - 1s - loss: 0.2937 - acc: 0.9181 - val_loss: 0.2866 - val_acc: 0.9202
Epoch 105/200
48000/48000 [==============================] - 1s - loss: 0.2934 - acc: 0.9181 - val_loss: 0.2863 - val_acc: 0.9198
Epoch 106/200
48000/48000 [==============================] - 1s - loss: 0.2931 - acc: 0.9186 - val_loss: 0.2862 - val_acc: 0.9201
Epoch 107/200
48000/48000 [==============================] - 1s - loss: 0.2928 - acc: 0.9186 - val_loss: 0.2859 - val_acc: 0.9203
Epoch 108/200
48000/48000 [==============================] - 1s - loss: 0.2925 - acc: 0.9183 - val_loss: 0.2856 - val_acc: 0.9204
Epoch 109/200
48000/48000 [==============================] - 1s - loss: 0.2922 - acc: 0.9187 - val_loss: 0.2855 - val_acc: 0.9202
Epoch 110/200
48000/48000 [==============================] - 1s - loss: 0.2920 - acc: 0.9188 - val_loss: 0.2853 - val_acc: 0.9202
Epoch 111/200
48000/48000 [==============================] - 1s - loss: 0.2917 - acc: 0.9185 - val_loss: 0.2852 - val_acc: 0.9203
Epoch 112/200
48000/48000 [==============================] - 1s - loss: 0.2914 - acc: 0.9187 - val_loss: 0.2849 - val_acc: 0.9201
Epoch 113/200
48000/48000 [==============================] - 1s - loss: 0.2912 - acc: 0.9191 - val_loss: 0.2847 - val_acc: 0.9203
Epoch 114/200
48000/48000 [==============================] - 1s - loss: 0.2909 - acc: 0.9190 - val_loss: 0.2845 - val_acc: 0.9205
Epoch 115/200
48000/48000 [==============================] - 1s - loss: 0.2906 - acc: 0.9189 - val_loss: 0.2844 - val_acc: 0.9207
Epoch 116/200
48000/48000 [==============================] - 1s - loss: 0.2904 - acc: 0.9190 - val_loss: 0.2842 - val_acc: 0.9204
Epoch 117/200
48000/48000 [==============================] - 1s - loss: 0.2901 - acc: 0.9191 - val_loss: 0.2840 - val_acc: 0.9211
Epoch 118/200
48000/48000 [==============================] - 1s - loss: 0.2898 - acc: 0.9192 - val_loss: 0.2840 - val_acc: 0.9208
Epoch 119/200
48000/48000 [==============================] - 1s - loss: 0.2896 - acc: 0.9193 - val_loss: 0.2838 - val_acc: 0.9207
Epoch 120/200
48000/48000 [==============================] - 1s - loss: 0.2894 - acc: 0.9193 - val_loss: 0.2835 - val_acc: 0.9210
Epoch 121/200
48000/48000 [==============================] - 1s - loss: 0.2891 - acc: 0.9192 - val_loss: 0.2833 - val_acc: 0.9209
Epoch 122/200
48000/48000 [==============================] - 1s - loss: 0.2889 - acc: 0.9196 - val_loss: 0.2832 - val_acc: 0.9212
Epoch 123/200
48000/48000 [==============================] - 1s - loss: 0.2886 - acc: 0.9195 - val_loss: 0.2831 - val_acc: 0.9208
Epoch 124/200
48000/48000 [==============================] - 1s - loss: 0.2884 - acc: 0.9194 - val_loss: 0.2829 - val_acc: 0.9210
Epoch 125/200
48000/48000 [==============================] - 1s - loss: 0.2882 - acc: 0.9196 - val_loss: 0.2827 - val_acc: 0.9212
Epoch 126/200
48000/48000 [==============================] - 1s - loss: 0.2880 - acc: 0.9196 - val_loss: 0.2826 - val_acc: 0.9210
Epoch 127/200
48000/48000 [==============================] - 1s - loss: 0.2877 - acc: 0.9198 - val_loss: 0.2825 - val_acc: 0.9216
Epoch 128/200
48000/48000 [==============================] - 1s - loss: 0.2875 - acc: 0.9199 - val_loss: 0.2823 - val_acc: 0.9212
Epoch 129/200
48000/48000 [==============================] - 1s - loss: 0.2873 - acc: 0.9199 - val_loss: 0.2822 - val_acc: 0.9212
Epoch 130/200
48000/48000 [==============================] - 1s - loss: 0.2871 - acc: 0.9201 - val_loss: 0.2820 - val_acc: 0.9216
Epoch 131/200
48000/48000 [==============================] - 1s - loss: 0.2868 - acc: 0.9202 - val_loss: 0.2819 - val_acc: 0.9214
Epoch 132/200
48000/48000 [==============================] - 1s - loss: 0.2867 - acc: 0.9200 - val_loss: 0.2817 - val_acc: 0.9216
Epoch 133/200
48000/48000 [==============================] - 1s - loss: 0.2864 - acc: 0.9199 - val_loss: 0.2815 - val_acc: 0.9217
Epoch 134/200
48000/48000 [==============================] - 1s - loss: 0.2862 - acc: 0.9202 - val_loss: 0.2814 - val_acc: 0.9213
Epoch 135/200
48000/48000 [==============================] - 1s - loss: 0.2860 - acc: 0.9202 - val_loss: 0.2813 - val_acc: 0.9216
Epoch 136/200
48000/48000 [==============================] - 1s - loss: 0.2858 - acc: 0.9204 - val_loss: 0.2812 - val_acc: 0.9214
Epoch 137/200
48000/48000 [==============================] - 1s - loss: 0.2856 - acc: 0.9203 - val_loss: 0.2811 - val_acc: 0.9216
Epoch 138/200
48000/48000 [==============================] - 1s - loss: 0.2854 - acc: 0.9202 - val_loss: 0.2809 - val_acc: 0.9216
Epoch 139/200
48000/48000 [==============================] - 1s - loss: 0.2852 - acc: 0.9205 - val_loss: 0.2808 - val_acc: 0.9218
Epoch 140/200
48000/48000 [==============================] - 1s - loss: 0.2850 - acc: 0.9206 - val_loss: 0.2807 - val_acc: 0.9217
Epoch 141/200
48000/48000 [==============================] - 1s - loss: 0.2848 - acc: 0.9208 - val_loss: 0.2806 - val_acc: 0.9217
Epoch 142/200
48000/48000 [==============================] - 1s - loss: 0.2846 - acc: 0.9206 - val_loss: 0.2804 - val_acc: 0.9220
Epoch 143/200
48000/48000 [==============================] - 1s - loss: 0.2844 - acc: 0.9203 - val_loss: 0.2802 - val_acc: 0.9219
Epoch 144/200
48000/48000 [==============================] - 1s - loss: 0.2842 - acc: 0.9208 - val_loss: 0.2801 - val_acc: 0.9222
Epoch 145/200
48000/48000 [==============================] - 1s - loss: 0.2841 - acc: 0.9207 - val_loss: 0.2800 - val_acc: 0.9221
Epoch 146/200
48000/48000 [==============================] - 1s - loss: 0.2839 - acc: 0.9209 - val_loss: 0.2799 - val_acc: 0.9222
Epoch 147/200
48000/48000 [==============================] - 1s - loss: 0.2837 - acc: 0.9209 - val_loss: 0.2798 - val_acc: 0.9221
Epoch 148/200
48000/48000 [==============================] - 1s - loss: 0.2835 - acc: 0.9209 - val_loss: 0.2797 - val_acc: 0.9225
Epoch 149/200
48000/48000 [==============================] - 1s - loss: 0.2833 - acc: 0.9209 - val_loss: 0.2796 - val_acc: 0.9222
Epoch 150/200
48000/48000 [==============================] - 1s - loss: 0.2831 - acc: 0.9212 - val_loss: 0.2795 - val_acc: 0.9220
Epoch 151/200
48000/48000 [==============================] - 1s - loss: 0.2830 - acc: 0.9210 - val_loss: 0.2793 - val_acc: 0.9219
Epoch 152/200
48000/48000 [==============================] - 1s - loss: 0.2828 - acc: 0.9208 - val_loss: 0.2793 - val_acc: 0.9222
Epoch 153/200
48000/48000 [==============================] - 1s - loss: 0.2826 - acc: 0.9213 - val_loss: 0.2792 - val_acc: 0.9222
Epoch 154/200
48000/48000 [==============================] - 1s - loss: 0.2824 - acc: 0.9212 - val_loss: 0.2790 - val_acc: 0.9224
Epoch 155/200
48000/48000 [==============================] - 1s - loss: 0.2823 - acc: 0.9213 - val_loss: 0.2789 - val_acc: 0.9226
Epoch 156/200
48000/48000 [==============================] - 1s - loss: 0.2821 - acc: 0.9214 - val_loss: 0.2787 - val_acc: 0.9224
Epoch 157/200
48000/48000 [==============================] - 1s - loss: 0.2819 - acc: 0.9216 - val_loss: 0.2787 - val_acc: 0.9223
Epoch 158/200
48000/48000 [==============================] - 1s - loss: 0.2818 - acc: 0.9214 - val_loss: 0.2786 - val_acc: 0.9223
Epoch 159/200
48000/48000 [==============================] - 1s - loss: 0.2816 - acc: 0.9216 - val_loss: 0.2785 - val_acc: 0.9225
Epoch 160/200
48000/48000 [==============================] - 1s - loss: 0.2815 - acc: 0.9217 - val_loss: 0.2784 - val_acc: 0.9223
Epoch 161/200
48000/48000 [==============================] - 1s - loss: 0.2813 - acc: 0.9217 - val_loss: 0.2783 - val_acc: 0.9224
Epoch 162/200
48000/48000 [==============================] - 1s - loss: 0.2811 - acc: 0.9219 - val_loss: 0.2782 - val_acc: 0.9224
Epoch 163/200
48000/48000 [==============================] - 1s - loss: 0.2809 - acc: 0.9215 - val_loss: 0.2781 - val_acc: 0.9226
Epoch 164/200
48000/48000 [==============================] - 1s - loss: 0.2808 - acc: 0.9218 - val_loss: 0.2780 - val_acc: 0.9227
Epoch 165/200
48000/48000 [==============================] - 1s - loss: 0.2806 - acc: 0.9218 - val_loss: 0.2780 - val_acc: 0.9224
Epoch 166/200
48000/48000 [==============================] - 1s - loss: 0.2805 - acc: 0.9218 - val_loss: 0.2777 - val_acc: 0.9228
Epoch 167/200
48000/48000 [==============================] - 1s - loss: 0.2803 - acc: 0.9219 - val_loss: 0.2777 - val_acc: 0.9227
Epoch 168/200
48000/48000 [==============================] - 1s - loss: 0.2801 - acc: 0.9219 - val_loss: 0.2777 - val_acc: 0.9223
Epoch 169/200
48000/48000 [==============================] - 1s - loss: 0.2800 - acc: 0.9223 - val_loss: 0.2775 - val_acc: 0.9228
Epoch 170/200
48000/48000 [==============================] - 1s - loss: 0.2799 - acc: 0.9221 - val_loss: 0.2774 - val_acc: 0.9227
Epoch 171/200
48000/48000 [==============================] - 1s - loss: 0.2797 - acc: 0.9220 - val_loss: 0.2773 - val_acc: 0.9232
Epoch 172/200
48000/48000 [==============================] - 1s - loss: 0.2796 - acc: 0.9220 - val_loss: 0.2773 - val_acc: 0.9230
Epoch 173/200
48000/48000 [==============================] - 1s - loss: 0.2794 - acc: 0.9221 - val_loss: 0.2772 - val_acc: 0.9227
Epoch 174/200
48000/48000 [==============================] - 1s - loss: 0.2793 - acc: 0.9221 - val_loss: 0.2771 - val_acc: 0.9229
Epoch 175/200
48000/48000 [==============================] - 1s - loss: 0.2791 - acc: 0.9225 - val_loss: 0.2770 - val_acc: 0.9232
Epoch 176/200
48000/48000 [==============================] - 1s - loss: 0.2790 - acc: 0.9225 - val_loss: 0.2769 - val_acc: 0.9232
Epoch 177/200
48000/48000 [==============================] - 1s - loss: 0.2788 - acc: 0.9222 - val_loss: 0.2768 - val_acc: 0.9227
Epoch 178/200
48000/48000 [==============================] - 1s - loss: 0.2787 - acc: 0.9225 - val_loss: 0.2767 - val_acc: 0.9229
Epoch 179/200
48000/48000 [==============================] - 1s - loss: 0.2786 - acc: 0.9224 - val_loss: 0.2767 - val_acc: 0.9229
Epoch 180/200
48000/48000 [==============================] - 1s - loss: 0.2784 - acc: 0.9226 - val_loss: 0.2766 - val_acc: 0.9230
Epoch 181/200
48000/48000 [==============================] - 1s - loss: 0.2783 - acc: 0.9228 - val_loss: 0.2765 - val_acc: 0.9232
Epoch 182/200
48000/48000 [==============================] - 1s - loss: 0.2781 - acc: 0.9226 - val_loss: 0.2764 - val_acc: 0.9232
Epoch 183/200
48000/48000 [==============================] - 1s - loss: 0.2780 - acc: 0.9227 - val_loss: 0.2764 - val_acc: 0.9230
Epoch 184/200
48000/48000 [==============================] - 1s - loss: 0.2779 - acc: 0.9227 - val_loss: 0.2762 - val_acc: 0.9231
Epoch 185/200
48000/48000 [==============================] - 1s - loss: 0.2777 - acc: 0.9229 - val_loss: 0.2762 - val_acc: 0.9230
Epoch 186/200
48000/48000 [==============================] - 1s - loss: 0.2776 - acc: 0.9229 - val_loss: 0.2761 - val_acc: 0.9233
Epoch 187/200
48000/48000 [==============================] - 1s - loss: 0.2775 - acc: 0.9228 - val_loss: 0.2760 - val_acc: 0.9232
Epoch 188/200
48000/48000 [==============================] - 1s - loss: 0.2774 - acc: 0.9229 - val_loss: 0.2759 - val_acc: 0.9233
Epoch 189/200
48000/48000 [==============================] - 1s - loss: 0.2772 - acc: 0.9229 - val_loss: 0.2758 - val_acc: 0.9232
Epoch 190/200
48000/48000 [==============================] - 1s - loss: 0.2771 - acc: 0.9230 - val_loss: 0.2758 - val_acc: 0.9232
Epoch 191/200
48000/48000 [==============================] - 1s - loss: 0.2770 - acc: 0.9230 - val_loss: 0.2757 - val_acc: 0.9232
Epoch 192/200
48000/48000 [==============================] - 1s - loss: 0.2768 - acc: 0.9229 - val_loss: 0.2756 - val_acc: 0.9236
Epoch 193/200
48000/48000 [==============================] - 1s - loss: 0.2767 - acc: 0.9230 - val_loss: 0.2756 - val_acc: 0.9234
Epoch 194/200
48000/48000 [==============================] - 1s - loss: 0.2766 - acc: 0.9229 - val_loss: 0.2755 - val_acc: 0.9234
Epoch 195/200
48000/48000 [==============================] - 1s - loss: 0.2765 - acc: 0.9231 - val_loss: 0.2754 - val_acc: 0.9237
Epoch 196/200
48000/48000 [==============================] - 1s - loss: 0.2764 - acc: 0.9232 - val_loss: 0.2754 - val_acc: 0.9237
Epoch 197/200
48000/48000 [==============================] - 1s - loss: 0.2762 - acc: 0.9230 - val_loss: 0.2753 - val_acc: 0.9237
Epoch 198/200
48000/48000 [==============================] - 1s - loss: 0.2761 - acc: 0.9231 - val_loss: 0.2752 - val_acc: 0.9242
Epoch 199/200
48000/48000 [==============================] - 1s - loss: 0.2760 - acc: 0.9233 - val_loss: 0.2751 - val_acc: 0.9238
Epoch 200/200
48000/48000 [==============================] - 1s - loss: 0.2758 - acc: 0.9231 - val_loss: 0.2751 - val_acc: 0.9237
Step 9: Evaluate the model on the test dataset (10,000 images)
score = model.evaluate(X_test, Y_test, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])
9856/10000 [============================>.] - ETA: 0s
Test score: 0.276437405369
Test accuracy: 0.9228
[Optional] Step 10: Save the model (serialized) to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
%ls
Volume in drive C is Windows
Volume Serial Number is 7252-C405
Directory of C:\Users\cobalt\workspace
09/17/2017 12:53 PM <DIR> .
09/17/2017 12:53 PM <DIR> ..
09/16/2017 11:08 PM <DIR> .ipynb_checkpoints
01/07/2017 12:22 PM <DIR> .metadata
09/17/2017 12:53 PM 48,179 DeepLearningHelloWorld.ipynb
01/08/2017 10:52 AM <DIR> Hello
01/08/2017 10:52 AM <DIR> Hellocpp11
01/09/2017 04:45 PM <DIR> HelloOpenCV
09/17/2017 12:53 PM 723 model.json
01/07/2017 12:22 PM <DIR> RemoteSystemsTempFiles
2 File(s) 48,902 bytes
8 Dir(s) 199,888,052,224 bytes free
[Optional] Step 11: Save the model weights
model.save_weights("model.h5")
%ls
Volume in drive C is Windows
Volume Serial Number is 7252-C405
Directory of C:\Users\cobalt\workspace
09/17/2017 01:07 PM <DIR> .
09/17/2017 01:07 PM <DIR> ..
09/16/2017 11:08 PM <DIR> .ipynb_checkpoints
01/07/2017 12:22 PM <DIR> .metadata
09/17/2017 12:55 PM 47,588 DeepLearningHelloWorld.ipynb
01/08/2017 10:52 AM <DIR> Hello
01/08/2017 10:52 AM <DIR> Hellocpp11
01/09/2017 04:45 PM <DIR> HelloOpenCV
09/17/2017 01:07 PM 42,528 model.h5
09/17/2017 12:53 PM 723 model.json
01/07/2017 12:22 PM <DIR> RemoteSystemsTempFiles
3 File(s) 90,839 bytes
8 Dir(s) 199,868,473,344 bytes free
[Optional] Step 12: Load the saved model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model.h5")
[Optional] Step 13: Compile and evaluate loaded model
loaded_model.compile(loss='categorical_crossentropy',
optimizer=OPTIMIZER,
metrics=['accuracy'])
score = loaded_model.evaluate(X_test, Y_test, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])
9440/10000 [===========================>..] - ETA: 0s
Test score: 0.276437405369
Test accuracy: 0.9228
- mkc
Written on September 17, 2017