accuracy 98%

master
li.chengmeng 3 years ago
parent b62ce1a612
commit 6061e80829

@ -1,21 +1,15 @@
import tensorflow as tf
from tensorflow import keras
config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(allow_growth=True))
sess = tf.compat.v1.Session(config=config)
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
print(train_images.shape)
import tensorflow as tf
from tensorflow import keras
num_mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = num_mnist.load_data()
train_images = train_images[:1000]
train_labels = train_labels[:1000]
test_images = train_images[:1000]
test_labels = train_images[:1000]
print(train_images.shape)
print(train_labels.shape)
print(test_images.shape)
print(test_labels.shape)
model = keras.Sequential()
model.add(keras.layers.Conv2D(8, (3,3), activation = 'relu', input_shape = (28,28,1)))
@ -30,7 +24,8 @@ model.add(keras.layers.Dense(36, activation = tf.nn.softmax))
train_images_scaled = train_images/255
model.compile(optimizer = 'adam', loss = tf.losses.sparse_categorical_crossentropy, metrics = ['accuracy'])
history = model.fit(train_images_scaled.reshape(-1, 28, 28 ,1), train_labels, epochs = 10, batch_size=8)
history = model.fit(train_images_scaled.reshape(-1, 28, 28 ,1), train_labels, epochs = 8)
results = model.evaluate(test_images.reshape(-1, 28, 28 ,1), test_labels)
#test_images_scaled = test_images/255
#results = model.evaluate(test_images_scaled.reshape(-1, 28, 28 ,1), test_labels)

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