By Salerno | March 14, 2020
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
predictions = model(x_train[:1]).numpy()## WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.
##
## If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
##
## To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.predictions## array([[-0.05698846, -0.41724604, 0.20074931, -0.89817846, -0.434907 ,
## 0.28240982, -0.03079729, 0.32150513, 0.17321926, 0.46897537]],
## dtype=float32)
tf.nn.softmax(predictions).numpy()## array([[0.0913271 , 0.06370035, 0.11817721, 0.03937998, 0.06258521,
## 0.12823261, 0.09375067, 0.13334519, 0.11496817, 0.15453358]],
## dtype=float32)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_fn(y_train[:1], predictions).numpy()## 2.0539095
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)## Train on 60000 samples
## Epoch 1/5
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## 60000/60000 [==============================] - 5s 80us/sample - loss: 0.2963 - accuracy: 0.9142
## Epoch 2/5
##
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## 60000/60000 [==============================] - 5s 76us/sample - loss: 0.1404 - accuracy: 0.9576
## Epoch 3/5
##
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## 60000/60000 [==============================] - 4s 75us/sample - loss: 0.1063 - accuracy: 0.9681
## Epoch 4/5
##
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## 60000/60000 [==============================] - 5s 78us/sample - loss: 0.0867 - accuracy: 0.9732
## Epoch 5/5
##
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## 60000/60000 [==============================] - 5s 76us/sample - loss: 0.0722 - accuracy: 0.9775
## <tensorflow.python.keras.callbacks.History object at 0x0000000030FCB648>
model.evaluate(x_test, y_test, verbose=2)## 10000/10000 - 0s - loss: 0.0785 - accuracy: 0.9758
## [0.07854731764825992, 0.9758]
probability_model = tf.keras.Sequential([
model,
tf.keras.layers.Softmax()
])
probability_model(x_test[:5])## <tf.Tensor: shape=(5, 10), dtype=float32, numpy=
## array([[2.3535537e-07, 1.1087331e-07, 9.0353415e-06, 5.1420293e-04,
## 1.9881577e-10, 7.3825049e-07, 2.8934629e-11, 9.9946576e-01,
## 5.2530334e-07, 9.4558809e-06],
## [2.3817480e-08, 1.7185285e-04, 9.9982351e-01, 4.2813531e-06,
## 5.3177487e-15, 1.2208089e-07, 2.7580992e-07, 2.4556867e-14,
## 3.9813731e-08, 8.9392667e-11],
## [5.8469413e-07, 9.9976522e-01, 6.9694550e-05, 1.2077020e-06,
## 2.1383299e-05, 3.3641725e-06, 5.9426807e-06, 9.1478352e-05,
## 4.0556668e-05, 6.5415355e-07],
## [9.9980491e-01, 4.8268034e-10, 1.4671631e-04, 2.2860399e-07,
## 3.0996057e-07, 6.2423592e-06, 2.7276790e-05, 1.4985205e-06,
## 2.2984095e-08, 1.2802517e-05],
## [2.9165078e-05, 3.0166632e-08, 6.4240016e-06, 5.6741047e-08,
## 9.9607170e-01, 1.0497470e-06, 1.4562958e-06, 9.0088892e-05,
## 3.5669891e-07, 3.7996941e-03]], dtype=float32)>comments powered by Disqus