feat: add tensorflow test script

.gitignore

+.idea

tensorflow/bert/infer.py

+import tensorflow as tf
+import tensorflow_hub as hub
+import tensorflow_text as text
+text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
+preprocessor = hub.KerasLayer(
+    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-preprocess/3")
+encoder_inputs = preprocessor(text_input)
+encoder = hub.KerasLayer(
+    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-l-12-h-768-a-12/3",
+    trainable=True)
+outputs = encoder(encoder_inputs)
+pooled_output = outputs["pooled_output"]      # [batch_size, 768].
+sequence_output = outputs["sequence_output"]  # [batch_size, seq_length, 768].
+embedding_model = tf.keras.Model(text_input, pooled_output)
+sentences = tf.constant(["(your text here)"])
+print(embedding_model(sentences))
\ No newline at end of file

tensorflow/mnist/train.py

+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, activation='softmax')
+])
+
+model.compile(optimizer='adam',
+  loss='sparse_categorical_crossentropy',
+  metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=5)
+model.evaluate(x_test, y_test)
\ No newline at end of file