Addition of Python4Delphi/Tensorflow-Demos

Author: hartmutdavid

P4DTensorflowDemos/Demos/Machine_Learning_with_TensorFlow/Chapter02/E00_overview.rtf

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+# # Tensor Types
+
+import tensorflow as tf
+import numpy as np
+
+# Define a 2x2 matrix in 3 different ways
+
+m1 = [[1.0, 2.0], [3.0, 4.0]]
+m2 = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+m3 = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+
+print(type(m1))
+print(type(m2))
+print(type(m3))
+
+
+# Create tensor objects out of various types
+
+t1 = tf.convert_to_tensor(m1, dtype=tf.float32)
+t2 = tf.convert_to_tensor(m2, dtype=tf.float32)
+t3 = tf.convert_to_tensor(m3, dtype=tf.float32)
+
+print(type(t1))
+print(type(t2))
+print(type(t3))

P4DTensorflowDemos/Demos/Machine_Learning_with_TensorFlow/Chapter02/E01_types.py

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+import tensorflow as tf
+
+x = tf.constant([[1, 2]])
+neg_x = tf.negative(x)
+
+print(neg_x)
+
+with tf.Session() as sess:
+  result = sess.run(neg_x)
+  print(result)

P4DTensorflowDemos/Demos/Machine_Learning_with_TensorFlow/Chapter02/E01_types_desc.rtf

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+import tensorflow as tf
+sess = tf.InteractiveSession()
+
+x = tf.constant([[1., 2.]])
+neg_op = tf.negative(x)
+
+result = neg_op.eval()
+print(result)
+
+sess.close()

P4DTensorflowDemos/Demos/Machine_Learning_with_TensorFlow/Chapter02/E02_main.py

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+import tensorflow as tf
+
+x = tf.constant([[1, 2]])
+neg_op = tf.negative(x)
+
+with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
+  result = sess.run(neg_op)
+  print(result)
+
+

P4DTensorflowDemos/Demos/Machine_Learning_with_TensorFlow/Chapter02/E02_main_desc.rtf

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+# # Using Variables in TensorFlow
+
+import tensorflow as tf
+sess = tf.InteractiveSession()
+
+# Create a boolean variable called `spike` to detect sudden a sudden increase in a series of numbers.
+# 
+# Since all variables must be initialized, initialize the variable by calling `run()` on its `initializer`.
+raw_data = [1., 2., 8., -1., 0., 5.5, 6., 13]
+spike = tf.Variable(False)
+spike.initializer.run()
+
+# Loop through the data and update the spike variable when there is a significant increase 
+for i in range(1, len(raw_data)):
+  if raw_data[i] - raw_data[i-1] > 5:
+    updater = tf.assign(spike, tf.constant(True))
+    updater.eval()
+  else:
+    tf.assign(spike, False).eval()
+  print("Spike", spike.eval())
+
+sess.close()