Refers begginers of upstream [1] [2] [1] https://www.tensorflow.org/install/pip [2] https://www.tensorflow.org/tutorials/quickstart/beginner
Signed-off-by: Hongxu Jia <hongxu....@windriver.com> --- BUILD.md | 97 ++++++++++++++++++++++++++++++++------------------------ 1 file changed, 55 insertions(+), 42 deletions(-) diff --git a/BUILD.md b/BUILD.md index da5a148..bd0f44a 100644 --- a/BUILD.md +++ b/BUILD.md @@ -41,75 +41,88 @@ $ runqemu qemux86-64 core-image-minimal slirp kvm qemuparams="-m 5120" ## 5. Verify the install ``` -root@qemux86-64:~# python3 -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))" -tf.Tensor(-604.65454, shape=(), dtype=float32) +root@qemux86-64:~# python3 -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" +tf.Tensor(-3304.6208, shape=(), dtype=float32) ``` ## 6. Run tutorial case ### Refer: https://www.tensorflow.org/tutorials ``` -root@qemux86-64:~# cat >code.py <<ENDOF +root@qemux86-64:~# cat >code-v2.py <<ENDOF import tensorflow as tf mnist = tf.keras.datasets.mnist -(x_train, y_train),(x_test, y_test) = mnist.load_data() +(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(512, activation=tf.nn.relu), + tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), - tf.keras.layers.Dense(10, activation=tf.nn.softmax) + tf.keras.layers.Dense(10) ]) + +predictions = model(x_train[:1]).numpy() +tf.nn.softmax(predictions).numpy() +loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) +loss_fn(y_train[:1], predictions).numpy() + model.compile(optimizer='adam', - loss='sparse_categorical_crossentropy', + loss=loss_fn, metrics=['accuracy']) - model.fit(x_train, y_train, epochs=5) -model.evaluate(x_test, y_test) +model.evaluate(x_test, y_test, verbose=2) + +probability_model = tf.keras.Sequential([ + model, + tf.keras.layers.Softmax() +]) +probability_model(x_test[:5]) + + ENDOF -root@qemux86-64:~# python3 ./code.py -Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz -11493376/11490434 [==============================] - 7s 1us/step -Instructions for updating: -Colocations handled automatically by placer. -Instructions for updating: -Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. +root@qemux86-64:~# python3 ./code-v2.py +2020-12-15 08:16:44.171593: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2) +2020-12-15 08:16:44.184464: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3099995000 Hz Epoch 1/5 -60000/60000 [==============================] - 27s 449us/sample - loss: 0.2211 - acc: 0.9346 +1875/1875 [==============================] - 14s 7ms/step - loss: 0.4833 - accuracy: 0.8595 Epoch 2/5 -60000/60000 [==============================] - 24s 408us/sample - loss: 0.0969 - acc: 0.9702 +1875/1875 [==============================] - 13s 7ms/step - loss: 0.1549 - accuracy: 0.9558 Epoch 3/5 -60000/60000 [==============================] - 26s 439us/sample - loss: 0.0694 - acc: 0.9780 +1875/1875 [==============================] - 13s 7ms/step - loss: 0.1135 - accuracy: 0.9651 Epoch 4/5 -60000/60000 [==============================] - 23s 390us/sample - loss: 0.0540 - acc: 0.9832 +1875/1875 [==============================] - 13s 7ms/step - loss: 0.0889 - accuracy: 0.9729 Epoch 5/5 -60000/60000 [==============================] - 24s 399us/sample - loss: 0.0447 - acc: 0.9851 -10000/10000 [==============================] - 1s 91us/sample - loss: 0.0700 - acc: 0.9782 +1875/1875 [==============================] - 13s 7ms/step - loss: 0.0741 - accuracy: 0.9777 +313/313 - 1s - loss: 0.0757 - accuracy: 0.9757 ``` ## 7. TensorFlow/TensorFlow Lite C++ Image Recognition Demo ``` root@qemux86-64:~# time label_image -2019-03-06 06:08:51.076028: I tensorflow/examples/label_image/main.cc:251] military uniform (653): 0.834306 -2019-03-06 06:08:51.078221: I tensorflow/examples/label_image/main.cc:251] mortarboard (668): 0.0218695 -2019-03-06 06:08:51.080054: I tensorflow/examples/label_image/main.cc:251] academic gown (401): 0.010358 -2019-03-06 06:08:51.081943: I tensorflow/examples/label_image/main.cc:251] pickelhaube (716): 0.00800814 -2019-03-06 06:08:51.083830: I tensorflow/examples/label_image/main.cc:251] bulletproof vest (466): 0.00535084 -real 0m 10.50s -user 0m 3.95s -sys 0m 6.46s +2020-12-15 08:18:34.853885: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3099995000 Hz +2020-12-15 08:18:41.565167: I tensorflow/examples/label_image/main.cc:252] military uniform (653): 0.834306 +2020-12-15 08:18:41.567874: I tensorflow/examples/label_image/main.cc:252] mortarboard (668): 0.0218696 +2020-12-15 08:18:41.568936: I tensorflow/examples/label_image/main.cc:252] academic gown (401): 0.0103581 +2020-12-15 08:18:41.569985: I tensorflow/examples/label_image/main.cc:252] pickelhaube (716): 0.00800819 +2020-12-15 08:18:41.571025: I tensorflow/examples/label_image/main.cc:252] bulletproof vest (466): 0.00535086 + +real 0m7.178s +user 0m6.101s +sys 0m0.893s + root@qemux86-64:~# time label_image.lite -Loaded model /usr/share/label_image/mobilenet_v1_1.0_224_quant.tflite -resolved reporter -invoked -average time: 1064.8 ms -0.780392: 653 military uniform -0.105882: 907 Windsor tie -0.0156863: 458 bow tie -0.0117647: 466 bulletproof vest -0.00784314: 835 suit -real 0m 1.10s -user 0m 1.07s -sys 0m 0.02s +INFO: Loaded model /usr/share/label_image/mobilenet_v1_1.0_224_quant.tflite +INFO: resolved reporter +INFO: invoked +INFO: average time: 213.584 ms +INFO: 0.780392: 653 military uniform +INFO: 0.105882: 907 Windsor tie +INFO: 0.0156863: 458 bow tie +INFO: 0.0117647: 466 bulletproof vest +INFO: 0.00784314: 835 suit + +real 0m0.233s +user 0m0.216s +sys 0m0.012s ``` -- 2.21.0
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