RE: Making Examples Run on ARMHF Machines

TensorFlow General Discussion
,
1

Hello,

I am working from here: examples/courses/udacity_intro_to_tensorflow_lite/tflite_c01_linear_regression.ipynb at master · tensorflow/examples · GitHub .

I am receiving this error:


  File "/home/debian/MyFirstTensor.py", line 52
    tf_results = model(tf.constant(input_data))
    ^
SyntaxError: invalid syntax

I tried some relative coding exchanges but nothing seems to be working. Does this Colab still work?

Seth

P.S. This is the entire source:


import tensorflow as tf
import pathlib
import numpy as np
import matplotlib.pyplot as plt

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input

# creating Keras Model!
x = [-1, 0, 1, 2, 3, 4]
y = [-3, -1, 1, 3, 5, 7]

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])
])

model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=200, verbose=1)

# generating the saved model!
export_dir = 'saved_model/1'
tf.saved_model.save(model, export_dir)

# converting the saved model!
converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model = converter.convert()

tflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)

# initialize the TFLite interpreter!

# Load the TFLite model and allocate tensors...
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()

# Getting input and output tensors...
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Testing the TensorFlow Lite model on random input data...
input_shape = input_details[0]['shape']
inputs, outputs = [], []
for _ in range(100):
    input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], input_data)

    interpreter.invoke()
    tflite_results = interpreter.get_tensor(output_details[0]['index']

    # Testing the Tensorflow model on random input data...
    tf_results = model(tf.constant(input_data))
    output_data = np.array(tf_results)

    inputs.append(input_data[0][0])
    outputs.append(output_data[0][0])

plt.plot(inputs, outputs, 'r')
plt.show

# download the TFLite model file!
try:
    from google.colab import files
    files.download(tflite_model_file)
except:
    pass
3

Hello,

Please forgive me and my attitude. I am done. The source works just fine.

Seth