Created
February 26, 2020 05:32
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# importing the libraries | |
from tensorflow.keras import datasets | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense | |
# loading dataset | |
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data() | |
# For training, we will use 10000 images | |
# And we will test our model on 1000 images | |
train_labels = train_labels[:10000] | |
test_labels = test_labels[:1000] | |
train_images = train_images[:10000].reshape(-1, 28 * 28) / 255.0 | |
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0 | |
# define the model | |
model = Sequential() | |
model.add(Dense(512, activation='relu', input_shape=(784,))) | |
model.add(Dense(10,activation='softmax')) | |
# compile the model | |
model.compile(optimizer='adam',loss= 'sparse_categorical_crossentropy',metrics=['accuracy']) | |
# model summary | |
model.summary() | |
# Train the model with the new callback | |
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images,test_labels)) | |
# Evaluate the model | |
loss, acc = model.evaluate(test_images, test_labels, verbose=2) | |
print("model, accuracy: {:5.2f}%".format(100*acc)) |
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