eager_basics.py

import tensorflow as tf

tf.enable_eager_execution()

print(tf.add(1, 2))
print(tf.add([1, 2], [3, 4]))
print(tf.square(5))
print(tf.reduce_sum([1, 2, 3]))
print(tf.encode_base64("hello world"))

# Operator overloading is also supported
print(tf.square(2) + tf.square(3))

x = tf.matmul([[1]], [[2, 3]])
print(x.shape)
print(x.dtype)


import numpy as np

ndarray = np.ones([3, 3])
print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)

print("And NumPy operations convert Tensors to numpy arrays automatically")
print(np.add(tensor, 1))

print("The .numpy() method explicitly converts a Tensor to a numpy array")
print(tensor.numpy())

x = tf.random_uniform([3, 3])

print("Is there a GPU available: "),
print(tf.test.is_gpu_available())

print("Is the Tensor on GPU #0: "),
print(x.device.endswith('GPU:0'))

import time


def time_matmul(x):
    start = time.time()
    for loop in range(10):
        tf.matmul(x, x)

    result = time.time() - start

    print("10 loops: {:0.2f}ms".format(1000 * result))


# Force execution on CPU
print("On CPU:")
with tf.device("CPU:0"):
    x = tf.random_uniform([1000, 1000])
    assert x.device.endswith("CPU:0")
    time_matmul(x)

# Force execution on GPU #0 if available
if tf.test.is_gpu_available():
    with tf.device("GPU:0"):
        x = tf.random_uniform([1000, 1000])
        assert x.device.endswith("GPU:0")
        time_matmul(x)

ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])

# Create a CSV file
import tempfile
_, filename = tempfile.mkstemp()

with open(filename, 'w') as f:
    f.write("""Line 1
Line2
Line3
""")

    ds_file = tf.data.TextLineDataset(filename)

ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)

ds_file = ds_file.batch(2)

print("Elements of ds_tensors:")
for x in ds_tensors:
    print(x)

print("\nElements in ds_file:")
for x in ds_file:
    print(x)