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ML/TensorFlow/Basics/tutorial7-indepth-functional.py

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+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers, regularizers
+from tensorflow.keras.datasets import mnist
+
+# Use Pandas to load dataset from csv file
+import pandas as pd
+
+# HYPERPARAMETERS
+BATCH_SIZE = 64
+WEIGHT_DECAY = 0.001
+LEARNING_RATE = 0.001
+
+# Make sure we don't get any GPU errors
+physical_devices = tf.config.list_physical_devices("GPU")
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+train_df = pd.read_csv("train.csv")
+test_df = pd.read_csv("test.csv")
+train_images = os.getcwd() + "/train_images/" + train_df.iloc[:, 0].values
+test_images = os.getcwd() + "/test_images/" + test_df.iloc[:, 0].values
+
+train_labels = train_df.iloc[:, 1:].values
+test_labels = test_df.iloc[:, 1:].values
+
+
+def read_image(image_path, label):
+    image = tf.io.read_file(image_path)
+    image = tf.image.decode_image(image, channels=1, dtype=tf.float32)
+
+    # In older versions you need to set shape in order to avoid error
+    # on newer (2.3.0+) the following 3 lines can safely be removed
+    image.set_shape((64, 64, 1))
+    label[0].set_shape([])
+    label[1].set_shape([])
+
+    labels = {"first_num": label[0], "second_num": label[1]}
+    return image, labels
+
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
+train_dataset = (
+    train_dataset.shuffle(buffer_size=len(train_labels))
+    .map(read_image)
+    .batch(batch_size=BATCH_SIZE)
+    .prefetch(buffer_size=AUTOTUNE)
+)
+
+test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels))
+test_dataset = (
+    test_dataset.map(read_image)
+    .batch(batch_size=BATCH_SIZE)
+    .prefetch(buffer_size=AUTOTUNE)
+)
+
+inputs = keras.Input(shape=(64, 64, 1))
+x = layers.Conv2D(
+    filters=32,
+    kernel_size=3,
+    padding="same",
+    kernel_regularizer=regularizers.l2(WEIGHT_DECAY),
+)(inputs)
+x = layers.BatchNormalization()(x)
+x = keras.activations.relu(x)
+x = layers.Conv2D(64, 3, kernel_regularizer=regularizers.l2(WEIGHT_DECAY),)(x)
+x = layers.BatchNormalization()(x)
+x = keras.activations.relu(x)
+x = layers.MaxPooling2D()(x)
+x = layers.Conv2D(
+    64, 3, activation="relu", kernel_regularizer=regularizers.l2(WEIGHT_DECAY),
+)(x)
+x = layers.Conv2D(128, 3, activation="relu")(x)
+x = layers.MaxPooling2D()(x)
+x = layers.Flatten()(x)
+x = layers.Dense(128, activation="relu")(x)
+x = layers.Dropout(0.5)(x)
+x = layers.Dense(64, activation="relu")(x)
+output1 = layers.Dense(10, activation="softmax", name="first_num")(x)
+output2 = layers.Dense(10, activation="softmax", name="second_num")(x)
+model = keras.Model(inputs=inputs, outputs=[output1, output2])
+
+model.compile(
+    optimizer=keras.optimizers.Adam(LEARNING_RATE),
+    loss=keras.losses.SparseCategoricalCrossentropy(),
+    metrics=["accuracy"],
+)
+
+model.fit(train_dataset, epochs=5, verbose=2)
+model.evaluate(test_dataset, verbose=2)