x_train, x_test = (x_train-min(x_train) / (max(x_train)-min(x_train) , (x_test-min(x_test) / (max(x_test)-min(x_test)
print("Eager:",tf.executing_eagerly())
print("GPU:",tf.test.is_gpu_available())#:with tf.device("/gpu:0"):
#tf.keras.backend.clear_session()
def create_model():
return tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
log_dir="..\\notebooks\logs\\" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
model.fit(x=x_train,
y=y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
#https://www.youtube.com/watch?v=B4p6gvPs-gM
!cd ../CellBender/examples/remove_background
!python generate_tiny_10x_pbmc.py
!$HOME/.local/bin/cellbender remove-background --input ./tiny_raw_gene_bc_matrices/GRCh38 --output ./tiny_10x_pbmc.h5 --expected-cells 500 --total-droplets-included 5000
