PyCharm Scientific Mode

main.py

import tensorflow as tf
from tensorflow.python.framework.ops import disable_eager_execution
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tensorflow.python.compiler.mlcompute import mlcompute

#%% activate eager execution.
disable_eager_execution()
mlcompute.set_mlc_device(device_name='gpu')

#%% loading the data
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()

#%% display one image.
image = X_train[785]
plt.imshow(image)
plt.show()

#%% data pre-processing
X_train = X_train / 255
X_test = X_test / 255

#%% model definition 
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

#%% compiling the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.summary()

#%% training the model
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))
loss, accuracy = model.evaluate(X_test, y_test)
print('Accuracy on test dataset:', accuracy)

#%% running predictions
predictions = model.predict(X_test)

#%% testing on a single image
np.argmax(model.predict(X_test[60].reshape(1, 28, 28)), axis=-1)

#%% creating the metrics dataframe
metrics_df = pd.DataFrame(history.history)
metrics_df.to_csv("/Users/derrickmwiti/PycharmProjects/Prediction/data/metrics.csv")

#%% Visualizing the training and validation oss
metrics_df[["loss", "val_loss"]].plot()
plt.show()
metrics_df[["accuracy", "val_accuracy"]].plot()
plt.show()

#%% the end