Here's some simple Tensorflow code to create a CNN, and train it.
import tensorflow as tf
import tensorflow.keras as keras
def create_model(optimizer="sgd"):
deep_model = keras.models.Sequential([
keras.layers.Conv2D(64, 7, activation="relu", padding="same",
input_shape=[1, 28, 28], name="input"),
keras.layers.MaxPooling2D(1,name="firstPool"),
keras.layers.Conv2D(128, 3, activation="relu", padding="same",
name="first_conv_1"),
keras.layers.Conv2D(128, 3, activation="relu", padding="same",
name="first_conv_2"),
keras.layers.MaxPooling2D(1, name="secondPool"),
keras.layers.Conv2D(256, 3, activation="relu", padding="same",
name="second_conv_1"),
keras.layers.Conv2D(256, 3, activation="relu", padding="same",
name="second_conv_2"),
keras.layers.MaxPooling2D(1, name="thirdPool"),
keras.layers.Flatten(name="flatten"),
keras.layers.Dense(128, activation="relu", name="pre-bottneck"),
keras.layers.Dropout(0.5, name="bottleneckDropout"),
keras.layers.Dense(64, activation="relu", name="bottleneck"),
keras.layers.Dropout(0.5, name="outputDropout"),
keras.layers.Dense(10, activation="softmax", name="output"),
])
deep_model.compile(loss="sparse_categorical_crossentropy",
optimizer=optimizer,
metrics=["accuracy"])
return deep_model
def fit_model(model, X_train, y_train, X_valid, y_valid, epochs):
history_conv = model.fit(X_train, y_train, validation_data=[X_valid, y_valid],
epochs=epochs, verbose=0)
return history_conv
def plot_history(history, name):
c10.plot_training(history, name, show=True)
model = create_model()
history = fit_model(model, X_train, y_train, X_valid, y_valid, epochs=10)
plot_history(history, "naive_deep_mnist")
When you run it, it causes this
enormous error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-95a7830ebd3c> in <module>
41
42 model = create_model()
---> 43 history = fit_model(model, X_train, y_train, X_valid, y_valid, epochs=10)
44 plot_history(history, "naive_deep_mnist")
<ipython-input-26-95a7830ebd3c> in fit_model(model, X_train, y_train, X_valid, y_valid, epochs)
34
35 def fit_model(model, X_train, y_train, X_valid, y_valid, epochs):
---> 36 history_conv = model.fit(X_train, y_train, validation_data=[X_valid, y_valid], epochs=epochs, verbose=0)
37 return history_conv
38
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695 self._concrete_stateful_fn = (
--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
697 *args, **kwds))
698
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,
3067 self._python_function,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x, training=True)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py:975 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/input_spec.py:191 assert_input_compatibility
raise ValueError('Input ' + str(input_index) + ' of layer ' +
ValueError: Input 0 of layer sequential_17 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [None, 28, 28]