Datasets:
HenryAI
/
KerasCodeExamples.txt

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axarr[a, b].xaxis.set_visible(False)
axarr[a, b].yaxis.set_visible(False)
plt.show()
png
Build the model
def conv_bn(x):
x = layers.Conv2D(filters=64, kernel_size=3, strides=2, padding=\"same\")(x)
x = layers.BatchNormalization()(x)
return layers.ReLU()(x)
inputs = layers.Input(shape=(28, 28, 1))
x = conv_bn(inputs)
x = conv_bn(x)
x = conv_bn(x)
x = conv_bn(x)
x = layers.Flatten()(x)
outputs = layers.Dense(classes, activation=\"softmax\")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile()
optimizer = keras.optimizers.SGD(learning_rate=learning_rate)
Train the model
training = []
testing = []
for meta_iter in range(meta_iters):
frac_done = meta_iter / meta_iters
cur_meta_step_size = (1 - frac_done) * meta_step_size
# Temporarily save the weights from the model.
old_vars = model.get_weights()
# Get a sample from the full dataset.
mini_dataset = train_dataset.get_mini_dataset(
inner_batch_size, inner_iters, train_shots, classes
)
for images, labels in mini_dataset:
with tf.GradientTape() as tape:
preds = model(images)
loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
grads = tape.gradient(loss, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
new_vars = model.get_weights()
# Perform SGD for the meta step.
for var in range(len(new_vars)):
new_vars[var] = old_vars[var] + (
(new_vars[var] - old_vars[var]) * cur_meta_step_size
)
# After the meta-learning step, reload the newly-trained weights into the model.
model.set_weights(new_vars)
# Evaluation loop
if meta_iter % eval_interval == 0:
accuracies = []
for dataset in (train_dataset, test_dataset):
# Sample a mini dataset from the full dataset.
train_set, test_images, test_labels = dataset.get_mini_dataset(
eval_batch_size, eval_iters, shots, classes, split=True
)
old_vars = model.get_weights()
# Train on the samples and get the resulting accuracies.
for images, labels in train_set:
with tf.GradientTape() as tape:
preds = model(images)
loss = keras.losses.sparse_categorical_crossentropy(labels, preds)
grads = tape.gradient(loss, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
test_preds = model.predict(test_images)
test_preds = tf.argmax(test_preds).numpy()
num_correct = (test_preds == test_labels).sum()
# Reset the weights after getting the evaluation accuracies.
model.set_weights(old_vars)
accuracies.append(num_correct / classes)
training.append(accuracies[0])
testing.append(accuracies[1])
if meta_iter % 100 == 0:
print(
\"batch %d: train=%f test=%f\" % (meta_iter, accuracies[0], accuracies[1])
)
batch 0: train=0.000000 test=0.600000
batch 100: train=0.600000 test=0.800000
batch 200: train=1.000000 test=0.600000
batch 300: train=0.600000 test=0.800000
batch 400: train=0.800000 test=1.000000
batch 500: train=1.000000 test=0.600000
batch 600: train=1.000000 test=1.000000
batch 700: train=1.000000 test=1.000000
batch 800: train=1.000000 test=0.600000
batch 900: train=1.000000 test=1.000000
batch 1000: train=0.800000 test=1.000000
batch 1100: train=1.000000 test=0.600000
batch 1200: train=0.800000 test=1.000000
batch 1300: train=0.800000 test=1.000000
batch 1400: train=1.000000 test=1.000000
batch 1500: train=0.800000 test=1.000000
batch 1600: train=1.000000 test=1.000000
batch 1700: train=1.000000 test=0.800000
batch 1800: train=1.000000 test=1.000000
batch 1900: train=0.800000 test=1.000000
Visualize Results
# First, some preprocessing to smooth the training and testing arrays for display.
window_length = 100
train_s = np.r_[