Model.Predict Python
# Import the libraries required in this example:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
x = layers.Dense(64, activation="relu", name="dense_2")(x)
outputs = layers.Dense(10, activation="softmax", name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data (NumPy arrays):
x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
# Allocate 10,000 samples for validation:
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]
model.compile(
optimizer=keras.optimizers.RMSprop(), # Optimizer
# Minimize loss:
loss=keras.losses.SparseCategoricalCrossentropy(),
# Monitor metrics:
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
print("Fit model on training data")
history = model.fit(
x_train,
y_train,
batch_size=64,
epochs=2,
# Validation of loss and metrics
# at the end of each epoch:
validation_data=(x_val, y_val),
)
history.history
print("Evaluate model on test data")
results = model.evaluate(x_test, y_test, batch_size=128)
print("test loss, test acc:", results)
# Generate a prediction using model.predict()
# and calculate it's shape:
print("Generate a prediction")
prediction = model.predict(x_test[:1])
print("prediction shape:", prediction.shape)
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