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