Open simplysameer333 opened 5 years ago
import tensorflow as tf
print("Version: ", tf.version)
mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.25), tf.keras.layers.Dense(10, activation='softmax') ])
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=10) model.evaluate(x_test, y_test)
import tensorflow as tf
print("Version: ", tf.version)
mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.25), tf.keras.layers.Dense(10, activation='softmax') ])
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=10) model.evaluate(x_test, y_test)