How to fix "symbolic tensors" use "steps_per_epoch" but not "batch_size" bug in a simple conv2d+liquid state machine net

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I am making a simple conv2d + dynamic reservoir (a customized recurrent layer with random / fixed connections that only outputs the last time step node states). The reservoir is written as a lambda layer to implement a simple equation as shown in the code. The model can be constructed by Keras.

I hope the model to be trained to classify some image sequences with a given batch size. (e.g. batch_size = 2) So ideally Keras should allocate batches of size 2x3x8x8x1 since the dataset is of the size 10x3x8x8x1. The time distributed Conv2d layer is supposed to return 2x3x6x6x3. The subsequent customized flatten layer should flat non-time dimensions and return 2x3x108. The reservior layer with 108 nodes should return 2x108. And the last read-out layer should return 2x5.

import keras
from keras.layers import Dense, Convolution2D, Activation, Lambda
from keras.layers.wrappers import TimeDistributed
from keras.models import Sequential

from keras import backend as K
import tensorflow as tf

import numpy as np

# Flatten the non-time dimensions
def flatten_tstep(x_in): # Input shape (None, 3, 6, 6, 3), Output shape (None, 3, 108)
    shape = K.shape( x_in ) # tensor shape
    x_out = K.reshape( x_in, [shape[0], shape[1], K.prod(shape[1:])] )
    return x_out

def flatten_tstep_shape( x_shape ) :
    n_batch, n_tsteps, n_rows, n_cols, n_filters = x_shape
    output_shape = ( n_batch, n_tsteps, n_rows * n_cols * n_filters ) # Flatten 
    return output_shape

# Simple Reservior
# Use a single batch as an example, the input (size 3x108) is of 3 time steps to the 108 nodes in the reserivor.
# The states of the nodes are stat_neuron (size 1x108)
# For t in range(3)
#   stat_neuron = stat_neuron * decay_coefficient + input[t, :] + recurrent_connection_matrix * stat_neuron
# End
# This layer effectively returns the states of the node in the last time step
def ag_reservior(x_in): # Input shape (None, 3, 108), Output shape (None, 108)
    shape = K.shape( x_in ) # tensor shape
    stat_neuron = K.zeros([shape[0], shape[2]]) # initialize Neuron states    
    t_step = tf.constant(0) # Initialize time counter, shifted by 1
    t_max = tf.subtract(shape[1], tf.constant(1)) # Maximum time steps, shifted by 1
    x = x_in
    def cond(t_step, t_max, stat_neuron, x):
        return tf.less(t_step, t_max)
    def body(t_step, t_max, stat_neuron, x):
        global RC_MATRIX, C_DECAY # Connection matrix, decay constant
        temp = tf.scalar_mul(C_DECAY, stat_neuron) #  stat_neuron * decay_coefficient    
        temp = tf.add(temp, x[:, t_step, :]) # stat_neuron * decay_coefficient + input[t, :]
        temp = tf.add(temp, tf.einsum('ij,bj->bi', RC_MATRIX, stat_neuron)) # out[batch,i]=sum_j RC_MATRIX[i,j]*stat_neuron[batch,j]
        return [tf.add(t_step, 1), t_max, temp, x]
    res = tf.while_loop(cond, body, [t_step, t_max, stat_neuron, x])
    return res[2]

def ag_reservior_shape( x_shape ) :
    in_batch, in_tsteps, in_nodes = x_shape
    output_shape = ( in_batch, in_nodes )
    return output_shape

#%% Parameters

n_sample = 10; # number of samples;
n_tstep = 3; # number of time steps per sample
n_row = 8; # number of rows per frame
n_col = 8; # number of columns per frame
n_channel = 1; # number of channel

RC_MATRIX = K.random_normal([108, 108]) # Reservior layer node recurrent connection matrix, note there are 108 nodes
C_DECAY = K.constant(0.9) # Recurrent layer node time-to-time decay coefficient

data = K.random_normal([n_sample, n_tstep, n_row, n_col, 1]) # Some random dataset
# data = np.random.randn(n_sample, n_tstep, n_row, n_col, 1)
label = np.random.randint(5, size=n_sample) # Some random dataset labels
label_onehot = K.one_hot(label, 5)

x_train = data
y_train = label_onehot

x_test = data
y_test = label_onehot

#%% Model

model=Sequential();

# Convolution Kernels: Input shape (batch_size, 3, 8, 8, 1), Output shape (batch_size, 3, 6, 6, 3)
model.add(TimeDistributed(Convolution2D(3, (3, 3), strides=1, padding='valid', use_bias=False, 
                                        kernel_initializer='random_uniform', trainable=False), input_shape = (n_tstep, n_row, n_col, n_channel)))

# Flatten non-time dimensions: Input shape (batch_size, 3, 6, 6, 3), Output shape (batch_size, 3, 108)
model.add(Lambda(flatten_tstep, output_shape = flatten_tstep_shape))

# Reservior: Input shape (batch_size 3, 108), Output shape (batch_size, 108)
model.add(Lambda(ag_reservior, output_shape = ag_reservior_shape))

# Reservior Read-out: Input shape (batch_size, 108), Output shape (batch_size, 5)
model.add(Dense(5, use_bias=False))
model.add(Activation('softmax'))

# Check model
model.summary()

#%% Training
opt = keras.optimizers.rmsprop(lr = 0.01, decay = 1e-6)
model.compile(loss='categorical_crossentropy', optimizer = opt, metrics = ['acc'])
history = model.fit(x_train, y_train, epochs = 50, validation_data = (x_test, y_test), batch_size = 2)

However, Keras said "If your data is in the form of symbolic tensors, you should specify the steps_per_epoch argument (instead of the batch_size argument, because symbolic tensors are expected to produce batches of input data)."

Could you advise on how to let Keras correctly recognize the batch size and proceed to the training? (Note that the Conv2d layer is fixed, the lambda layers are also fixed, only the last dense layer needs training.)

Thank you in advance.


That error means that one of your data tensors that is being used by Fit() is a symbolic tensor. The one hot label function returns a symbolic tensor. Try something like:

label_onehot = tf.Session().run(K.one_hot(label, 5))

I haven't personally tried this with Keras directly -- if it doesn't work with Keras, try using the tf one hot function instead of the Keras one hot function.

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It's resolved by using this code...

import keras
from keras.datasets import mnist
from keras.layers import Convolution2D, Dense, Flatten, Activation, Lambda
from keras.layers.wrappers import TimeDistributed
from keras.models import Sequential
import scipy.io

from keras import backend as K
import tensorflow as tf

import numpy as np
import matplotlib.pyplot as plt 

# Simple Reservior
# Use a single batch as an example, the input (size 3x108) is of 3 time steps to the 108 nodes in the reserivor.
# The states of the nodes are stat_neuron (size 1x108)
# For t in range(3)
#   stat_neuron = stat_neuron * decay_coefficient + input[t, :] + recurrent_connection_matrix * stat_neuron
# End
# This layer effectively returns the states of the node in the last time step
def ag_reservior(x_in): # Input shape (None, 3, 108), Output shape (None, 108)
    shape = K.shape( x_in ) # tensor shape
    stat_neuron = K.zeros([shape[0], shape[2]]) # initialize Neuron states    
    t_step = tf.constant(0) # Initialize time counter, shifted by 1
    t_max = shape[1] # Maximum time steps, shifted by 1
    x = x_in
    def cond(t_step, t_max, stat_neuron, x):
        return tf.less(t_step, t_max)
    def body(t_step, t_max, stat_neuron, x):
        global RC_MATRIX, C_DECAY # Connection matrix, decay constant
        temp = tf.scalar_mul(C_DECAY, stat_neuron) #  stat_neuron * decay_coefficient    
        temp = tf.add(temp, x[:, t_step, :]) # stat_neuron * decay_coefficient + input[t, :]
        temp = tf.add(temp, tf.einsum('ij,bj->bi', RC_MATRIX, stat_neuron)) # out[batch,i]=sum_j RC_MATRIX[i,j]*stat_neuron[batch,j]
        return [tf.add(t_step, 1), t_max, temp, x]
    res = tf.while_loop(cond, body, [t_step, t_max, stat_neuron, x])
    return res[2]

def ag_reservior_shape( x_shape ) :
    in_batch, in_tsteps, in_nodes = x_shape
    output_shape = ( in_batch, in_nodes )
    return output_shape

#%% Parameters

n_neurons = 4096; # number of neurons in the reservoir (same with the last dim of the flatten layer);

RC_MATRIX = K.random_normal([n_neurons, n_neurons], mean=0, stddev=1/n_neurons) # Reservior layer node recurrent connection matrix
C_DECAY = K.constant(0.5) # Diffusive memristor time-to-time decay coefficient

# Load training data from the .mat file
mat_contents = scipy.io.loadmat('mnist_sequence_kerasimport.mat')
x_train = mat_contents['xs_train']
x_test = mat_contents['xs_test']
y_train = mat_contents['ys_train']
y_test = mat_contents['ys_test']
# Reshape x_train, x_test into 5D array
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], x_train.shape[3], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], x_test.shape[3], 1)

#%% Model

model=Sequential();

# Convolution Kernels: Input shape (batch_size, 3, 8, 8, 1), Output shape (batch_size, 3, 8, 8, 64)
model.add(TimeDistributed(Convolution2D(64, (3, 3), strides=1, padding='same', use_bias=False, 
                                        kernel_initializer='random_uniform', trainable=False), input_shape = (x_train.shape[1:])))

model.add(TimeDistributed(Flatten()))

# Reservior: Input shape (batch_size 3, 108), Output shape (batch_size, 108)
model.add(Lambda(ag_reservior, output_shape = ag_reservior_shape))

# Reservior Read-out: Input shape (batch_size, 108), Output shape (batch_size, 5)
model.add(Dense(6, use_bias=False))
model.add(Activation('softmax'))

# Check model
model.summary()

#%% Training
opt = keras.optimizers.rmsprop(lr = 0.01, decay = 1e-6)
model.compile(loss='categorical_crossentropy', optimizer = opt, metrics = ['acc'])
history = model.fit(x_train, y_train, epochs = 2, validation_data = (x_test, y_test), batch_size = 50)

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Try to use the eval() or numpy() function on your tensors, so they are converted to numpy arrays.

check: How can I convert a tensor into a numpy array in TensorFlow?

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