def Conf_mat():
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
loss_function = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
inputs = tf.keras.Input(shape=[32, 32, 3])
x = Convolve([4, 4, 3, 32])(inputs)
x = Convolve([4, 4, 32, 64])(x)
x = Convolve([4, 4, 64, 128])(x)
x = Flatten([-1, 4 * 4 * 128])(x)
x = FC([4 * 4 * 128, 1024])(x)
x = FC([1024, 10])(x)
outputs = Softmax([])(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
print(model.summary())
model.compile(optimizer=optimizer,
loss=loss_function,
metrics=['accuracy'])
model.load_weights("Graphs_and_Results/best_weights.h5")
datafeeder = Prep()
data, label = datafeeder.nextBatchTest()
acc = model.evaluate(data, label, batch_size=100)
print(acc)
def Big_Train(sess):
sess.run(tf.global_variables_initializer())
writer = tf.compat.v1.summary.FileWriter(
"Graphs_and_Results/",
sess.graph) # this will write summary tensorboard
datafeeder = Prep()
display, _ = datafeeder.nextBatchTrain(10)
tf.compat.v1.summary.image("10 training data examples",
display,
max_outputs=10)
for i in range(501):
data, label = datafeeder.nextBatchTrain(100)
prediction_, loss_, summary, _ = sess.run(
[prediction, loss, summary_op, train],
feed_dict={
x: data,
truth: label,
hold_prob: 1
})
print("Epoch: {}. Loss: {}".format(i, loss_))
if i % 10 == 0:
writer.add_summary(summary, global_step=i)
if i % 100 == 0 and i > 0:
saver.save(sess, "Graphs_and_Results/CNN_test", global_step=i)
data, label = datafeeder.nextBatchTest()
correct = 0
prediction_ = sess.run(prediction,
feed_dict={
x: data,
truth: label,
hold_prob: 1
})
for l in range(len(label)):
if (np.argmax(prediction_[l]) == np.argmax(label[l])):
correct += 1
print("This is the accuracy: {}".format(correct /
len(prediction_)))