Convert tflearn to keras - keras

Question

Since tflearn is outdated and I am watching a chatbot tutorial that uses tflearn, I want to write the neural network model in keras. However, I got this error right here:
WARNING:tensorflow:Model was constructed with shape (None, 58) for input KerasTensor(type_spec=TensorSpec(shape=(None, 58), dtype=tf.float32, name='input_22'), name='input_22', description="created by layer 'input_22'"), but it was called on an input with incompatible shape (None,).
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-164-1a494613d0d2> in <module>()
1 convert_input("Hello")
----> 2 chat()
2 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1801, in predict_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1790, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1783, in run_step **
outputs = model.predict_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1751, in predict_step
return self(x, training=False)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 228, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "sequential_24" (type Sequential).
Input 0 of layer "dense_59" is incompatible with the layer: expected min_ndim=2, found ndim=1. Full shape received: (None,)
Call arguments received:
• inputs=('tf.Tensor(shape=(None,), dtype=int64)',)
• training=False
• mask=None
I have tried to set up the keras model myself
model = keras.Sequential()
# model.add(keras.layers.InputLayer(input_shape = len(words)))
model.add(keras.Input(shape=len(training[0])))
model.add(keras.layers.Dense(8, activation = "relu"))
model.add(keras.layers.Dense(8, activation = "relu"))
model.add(keras.layers.Dense(len(output[0]), activation= "softmax"))
model.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics=['accuracy'])
# convert_input(inp) -> return a 1D numpy array filled with 1 and 0
prediction = model.predict([convert_input(inp)])
versus the tflearn model
network = tflearn.input_data(shape=[None, len(training[0])])
network = tflearn.fully_connected(network, 8)
network = tflearn.fully_connected(network, 8)
network = tflearn.fully_connected(network, len(output[0]), activation = "softmax")
network = tflearn.regression(network)
model = tflearn.DNN(network)
model.fit(training, output, n_epoch = 1000, batch_size=8, show_metric=True)
# convert_input(inp) -> return a 1D numpy array filled with 1 and 0
prediction = model.predict([convert_input(inp)])
However, when I call model.predict only the tflearn model works and not the keras. Please help!

Answer 1

I was in a similar predicament. However I was finally able to resolve my problem with the following code:
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
model=Sequential()
model.add(Dense(8,input_shape=(len(train_x[0]),),kernel_initializer='normal'))
model.add(Dense(8,kernel_initializer='normal'))
model.add(Dense(len(train_y[0]),activation='softmax',kernel_initializer='normal'))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(np.array(train_x),np.array(train_y), epochs=1000, batch_size=8, verbose=1)
During prediction,
model.predict(np.array([p]))
Basically converting the list into an array using np.array() helped me solve the problem.