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import numpy as np
import matplotlib.pyplot as plt
import time
from huggingface_hub import push_to_hub_keras
from tensorflow import keras
from tensorflow.keras import layers
# Model / data parameters
num_classes = 9
input_shape = (28, 28, 3)
batch_size = 1000
epochs =
# Define baseline model
def baseline_model():
# Create model
model = keras.Sequential(
[
keras.Input(shape=input_shape),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(num_classes, activation="softmax"),
]
)
model.summary()
# Compile model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
# Load Data
path = './pathmnist.npz'
with np.load(path) as data:
x_train = data['train_images']
y_train = data['train_labels']
x_test = data['test_images']
y_test = data['test_labels']
x_val = data['val_images']
y_val = data['val_labels']
# Show DataSet Images
for image in x_train:
plt.imshow(image)
plt.show()
break
# Normalize images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
x_val = x_val.astype("float32") / 255
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")
print(x_val.shape[0], "test samples")
# Convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
model = baseline_model()
# Fit model
#history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
inicio = time.time()
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val))
fin = time.time()
print(fin-inicio)
# Evaluation of the model
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1]) |